how does a hypothesis help scientists understand the natural world

• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• Games & Quizzes
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center

• When did science begin?
• Where was science invented?

scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

Sign up for the Live Science daily newsletter now

Get the world’s most fascinating discoveries delivered straight to your inbox.

Humans have long been a 'geophysical force on a planetary scale,' says philosopher Timothy Morton. That's neither good nor bad.

Drinking wastewater, building an island from scratch and creating an urban forest: 3 bold ways cities are already adapting to climate change

2,700-year-old shields and helmet from ancient kingdom unearthed at castle in Turkey

Most Popular

• 2 James Webb Telescope goes 'extreme' and spots baby stars at the edge of the Milky Way (image)
• 3 Why can't you suffocate by holding your breath?
• 4 Space photo of the week: Entangled galaxies form cosmic smiley face in new James Webb telescope image
• 5 Did Roman gladiators really fight to the death?

• Skip to primary navigation
• Skip to main content
• Skip to primary sidebar
• Skip to footer

Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions
• Science aims to build knowledge about the natural world.
• This knowledge is open to question and revision as we come up with new ideas and discover new evidence.
• Because it has been tested, scientific knowledge is reliable.

Misconception:  Scientific ideas are absolute and unchanging.

Misconception:  Because scientific ideas are tentative and subject to change, they can’t be trusted.

Correction:  Accepted scientific ideas are well-supported and reliable, but could be revised if warranted by the evidence.  Read more about it.

Science aims to explain and understand

The knowledge that is built by science is always open to question and revision. No scientific idea is ever once-and-for-all “proven” Why not? Well, science is constantly seeking new  evidence , which could reveal problems with our current understandings. Ideas that we fully  accept  today may be rejected or modified in light of new evidence discovered tomorrow. For example, up until 1938, paleontologists accepted the idea that coelacanths (an ancient fish) went extinct at the time that they last appear in the fossil record — about 80 million years ago. But that year, a live coelacanth was discovered off the coast of South Africa, causing scientists to revise their ideas and to investigate how this animal survives in the deep sea.

Despite the fact that they are subject to change, scientific ideas are reliable. The ideas that have gained scientific acceptance have done so because they are supported by many lines of evidence and have generated many expectations that hold true. Such scientific ideas allow us to figure out how entities in the natural world are likely to behave (e.g., how likely it is that a child will inherit a particular genetic disease) and how we can harness that understanding to solve problems (e.g., how electricity, wire, glass, and various compounds can be fashioned into a working light bulb). For example, scientific understandings of motion and gases allow us to build airplanes that reliably get us from one airport to the next. Though the knowledge used to design airplanes could be modified and built upon, it is also reliable. Time and time again, that knowledge has allowed us to produce airplanes that fly. We have good reason to trust accepted scientific ideas: they work!

A SCIENCE PROTOTYPE: RUTHERFORD AND THE ATOM

Ernest Rutherford’s investigations were aimed at understanding a small, but illuminating, corner of the natural world: the atom. He investigated this world using alpha particles, which are helium atoms stripped of their electrons. Rutherford had found that when a beam of these tiny, positively-charged alpha particles is fired through gold foil, the particles don’t stay on their beeline course, but change direction when passing through the foil. Rutherford wanted to figure out what this might tell him about the layout of an atom.

Rutherford’s story continues as we examine each item on the Science Checklist. To find out how this investigation measures up against the rest of the checklist, read on.

• Take a sidetrip
• Teaching resources

Science builds reliable knowledge, but does it seek the truth? Learn more in our side trip on  The many meanings of truth .

• Learn strategies for building lessons and activities around the Science Checklist: Grades 6-8 Grades 9-12 Grades 13-16
• Get  graphics and pdfs of the Science Checklist  to use in your classroom.

A science checklist

Science works with testable ideas

Subscribe to our newsletter

• The science flowchart
• Science stories
• Grade-level teaching guides
• Teaching resource database
• Journaling tool

What is Earth Science?

The nature of science, lesson objectives.

• Identify the goal of science.
• Explain the importance of asking questions.
• Describe how scientists study the natural world.
• Explain how and why scientists collect data.
• Describe the three major types of scientific models.
• Explain how a scientific theory differs from a hypothesis.
• Describe appropriate safety precautions inside and outside the science laboratory.
• conceptual model
• dependent variable
• independent variable
• mathematical model
• physical model
• scientific method

Introduction

Science is a path to gaining knowledge about the natural world. The study of science also includes the body of knowledge that has been collected through scientific inquiry.

To conduct a scientific investigation, scientists ask testable questions. To answer those questions, they make systematic observations and carefully collect relevant evidence. Then they use logical reasoning and some imagination to develop hypotheses and explanations. Finally, scientists design and conduct experiments based on their hypotheses.

Goal of Science

Scientists seek to understand the natural world. Scientists begin with a question and then try to answer the question with evidence and logic. A scientific question must be testable. It does not rely on faith or opinion. Our understanding of natural Earth processes help us to understand why earthquakes occur where they do and to understand the consequences of adding excess greenhouse gases to our atmosphere.

Scientific research may be done to build knowledge or to solve problems. Scientific discoveries may lead to technological advances. Pure research often aids in the development of applied research. Sometimes the results of pure research may be applied long after the pure research was completed. Sometimes something unexpected is discovered while scientists are conducting their research.

Some ideas are not testable. For example, supernatural phenomena, such as stories of ghosts, werewolves, or vampires, cannot be tested. Look at this website to see why astrology is not scientific .

Scientists describe what they see, whether in nature or in a laboratory. Science is the realm of facts and observations. However, science does not make moral judgments, such as “It is bad that the volcano erupted” and opinions are not relevant to scientific inquiry. Scientists might enjoy studying tornadoes, but their opinion that tornadoes are exciting is not important to learning about them. Scientists increase our technological knowledge, but science does not determine how or if we use that knowledge. Scientists learned to build an atomic bomb, but scientists didn’t decide whether or when to use it. Scientists have accumulated data on warming temperatures. Their models have shown the likely causes of this warming. But although scientists are largely in agreement on the causes of global warming, they can’t force politicians or individuals to pass laws or change behaviors.

For science to work, scientists must make some assumptions. The rules of nature, whether simple or complex, are the same everywhere in the universe. Natural events, structures, and landforms have natural causes. Evidence from the natural world can be used to learn about those causes. The objects and events in nature can be understood through careful, systematic study. Scientific ideas can change if we gather new data or learn more. An idea, even one that is accepted today, may need to be changed slightly or be entirely replaced if new evidence is found that contradicts it. Scientific knowledge can withstand the test of time. Accepted ideas in science become more reliable as they survive more tests.

Scientific Method

You have probably learned that the scientific method is the way scientists approach their work. The scientific method is a series of steps that help to investigate a question. Scientists use data and evidence gathered from observations, experience, or experiments to answer their questions.

But scientific inquiry rarely proceeds in the same sequence of steps outlined by the scientific method. For example, the order of the steps might change because more questions arise from the data that is collected. Still, to come to verifiable conclusions, logical, repeatable steps of the scientific method must be followed, as seen in the Figure below .

Book contents

• Understanding How Science Explains the World
• Series page
• Copyright page
• Acknowledgments
• 1 Why Explanation Matters in Science
• 2 The General Nature of Explanation
• 3 Specific Kinds of Scientific Explanations
• 4 Explanation and Prediction
• 5 Evaluating Explanations
• 6 Explanatory Quality and Felt Understanding
• 7 False Theories, But Accurate Explanations?
• 8 From Explanation to Knowledge
• Concluding Remarks
• Summary of Common Misunderstandings
• References and Further Reading
• Figure Credits

1 - Why Explanation Matters in Science

Published online by Cambridge University Press:  17 June 2022

While it isn’t necessary to do so, it’s often good to start a book by saying something that is clearly true. So, let’s do that. Science has had (and continues to have) a significant impact upon our lives. This fact is undeniable. Science has revealed to us how different species arise, the causes of our world’s changing climate, many of the microphysical particles that constitute all matter, among many other things. Science has made possible technology that has put computing power that was almost unimaginable a few decades ago literally in the palms of our hands. A common smartphone today has more computing power than the computers that NASA used to put astronauts on the Moon in 1969! There are, of course, many additional ways in which science has solved various problems and penetrated previously mysterious phenomena. A natural question to ask at this point is: why discuss this? While we all (or at least the vast majority of us!) appreciate science and what it has accomplished for modern society, there remain – especially among portions of the general public – confusions about science, how it works and what it aims to achieve. The primary goal of this book is to help address some specific confusions about one key aspect of science: how it explains the world.

The Primary Aims of Science

A first step in getting clearer on how science explains the world is to consider why science even attempts to explain the world. What exactly does science try to achieve? Or, perhaps putting the question more accurately, what do we (humans) seek to accomplish by employing the methods of science? It is widely accepted that there are three primary aims of scientific activity: prediction, control, and explanation of natural phenomena. Different domains of science emphasize some of these aims more than others. For instance, paleontologists don’t spend a lot of time focusing on controlling phenomena, whereas biomedical researchers devote a tremendous amount of effort to controlling infections and diseases. Despite these differences in emphasis, explanation is a common thread linking all these aims. For this reason, it isn’t uncommon to hold explanation to be the most important of these three primary aims of science. As the US National Research Council has said, “the goal of science is the construction of theories that can provide explanatory accounts of features of the world.”

What makes explanation so important to science? The answer lies in what successful scientific explanations give us: understanding. Very roughly, understanding arises when we grasp how various features of the world depend upon one another. When we come upon the scientific explanation of some phenomenon, our understanding of the world increases. By virtue of this increased understanding, we are often able to better predict and control phenomena. For example, having scientific explanations of why and how something like the 2019 novel coronavirus (SARS-CoV-2, the virus responsible for the COVID-19 pandemic) evolved, helps us to better understand the mechanisms by which this virus reproduces and is transmitted. Of course, once we understand how this virus is transmitted from person to person, we can predict which situations are likely to increase or decrease its spread, as well as when we are apt to see significant rises in the number of infected people. Additionally, this understanding can allow us to put into place guidelines that (if followed!) may help control the spread of the virus. Furthermore, it is understanding of SARS-CoV-2 that has allowed us to produce effective vaccines. Without such understanding, it is difficult to see how we could manage any of these feats.

Considering the role that understanding plays in both prediction and control, it is maybe a bit misleading to characterize science as having three primary aims as we did above. P. W. Bridgman, a Nobel Prize–winning physicist, once said “The act of understanding is at the heart of all scientific activity.” Another Nobel Prize laureate, Erwin Schrödinger, claimed that the foundation of the entire modern scientific worldview rests upon the “hypothesis that the display of Nature can be understood.” Understanding is central to science, and perhaps it is most accurate to say that the primary epistemic (pertaining to knowledge/cognitive success) aim of science is to produce understanding via scientific explanations. Using the understanding gained via scientific explanations to yield accurate predictions and to allow for increased control of phenomena are important secondary aims of science. There are, of course, important caveats and qualifications of this relationship between the goals of science. For instance, science often makes use of models (representations of events/phenomena in the world) in order to explain and predict phenomena. In many cases, however, we might be forced to make choices between models that offer better scientific explanations and models that make more accurate predictions. This trade-off is especially clear when we look at what is called “robustness analysis,” which is common in climate science. Robustness analysis involves analyzing a number of incompatible models (i.e., models that make different assumptions about the phenomena being modeled) in order to come up with predictions. In many cases robustness analysis leads to predictions that are considerably more accurate than can be achieved by looking at a single model. However, this often comes at the expense of explanation because we can’t really explain what is going on by consulting models that disagree with one another. Hence, at times we seem forced to choose between having better explanations of important phenomena or being able to make more accurate predictions about those phenomena.

For now, we can set aside this and other concerns (we will come back to them later) and consider the general picture of science that emerges when we consider its primary aim(s). Understanding is the central aim of science, and we gain understanding in science by way of scientific explanations. As is often the case, with new insights come new questions. What exactly is understanding? What are scientific explanations, and when are they successful? How do scientific explanations, when they are successful, provide us with understanding? We devote considerable attention to answering these, and many other, questions throughout this book. For the remainder of this chapter, the goal will be to get a firmer grip on the general ideas that will be more fully explored later.

Scientific Explanation

The nature of scientific explanation was a major focus of philosophy of science in the twentieth century, since at least the late 1940s. And, given the difficulties of philosophical analysis of important concepts and the nuances of explanation in science, it is unsurprising that the nature of scientific explanation remains a topic of much debate among philosophers of science even today. Over the course of the history of this discussion there have been many accounts of the nature of scientific explanation and a lot of objections, rebuttals, revisions, and developments of various theories. In fact, there have been too many theories of scientific explanation for us to explore or give much of an overview of even just the most influential accounts here. However, scientists have been using scientific explanations to better understand the world since science began. Furthermore, they have been evaluating scientific explanations to great success – and, there’s no reason to think that they’ll stop succeeding in this way. Importantly, all of this progress in terms of developing, testing, and evaluating scientific explanations has taken place despite it remaining unsettled which account of the general nature of scientific explanation is correct.

This notwithstanding, a plausible working model of scientific explanation will be helpful to have in hand. This should be ecumenical in the sense that it is at least compatible with the major theories of scientific explanation that have been put forward. Here is such a model: scientific explanation is a matter of tracking dependence relations. The idea here is that a scientific explanation consists of information about how or why one thing depends upon other things. Importantly, this “dependence” view of scientific explanation allows for all sorts of relations – causal relations (when something causes something else), constitution relations (when some things make up something else), mereological relations (relations that exist between the parts of an object), and so on – to count as explanatory. As a result of this, the dependence view of scientific explanation is consistent with all the major views of the nature of scientific explanation that have arisen in the philosophical literature.

Let us consider very briefly one scientific explanation on this model. Consider, for example, cystic fibrosis. People with this disease produce an excess of mucus that can cause passageways in the lungs to clog and obstructions to form in the pancreas. What explains why a particular person has cystic fibrosis? Mutations of the cystic fibrosis transmembrane conductance regulator ( CFTR ) gene. In terms of the dependence view of scientific explanation, we have a (at least partial) scientific explanation of cystic fibrosis. The scientific explanation of a particular person having cystic fibrosis (what is known as the explanandum – what is being explained) consists of information about how the person’s having this disease is dependent upon other things, in this case the person’s having mutated alleles of the CFTR gene (this is the explanans – what does the explaining, in other words, what provides the information about how or why the explanandum depends upon other things).

Here’s a very brief recap. Our general picture of scientific explanation is that it consists of information about dependence relations that exist between a particular phenomenon (whether it is a general process or a particular event) and other phenomena. More specifically, we have a scientific explanation of X when we have information about how or why X depends upon some other things, such as Y. In this sense, the explanation can be represented in a question-and-answer format: “Why X? Because of Y,” where X is the explanandum and Y the explanans.

Scientific Understanding

We’ve mentioned that understanding is the key epistemic goal of science. Let’s take some time to get a bit clearer about what we mean by understanding in this context. We use the term “understanding” in myriad ways. For instance, we sometimes say things like “ I understand that you’re angry with me” as a way of expressing a belief that we have while hedging a bit. We’re letting the other person know that we think that they are angry with us, but we don’t want to fully commit to being correct about this. We might also use “understand” in a way that is synonymous with knowing that something is a fact. “I understand that humans have 23 pairs of chromosomes” is just another way of expressing “I know that humans have 23 pairs of chromosomes.” Finally, we might experience an “aha” moment in which a particular scientific explanation feels as if it is giving us insight into the workings of the world. We might be tempted to call this feeling itself understanding. Even if “understanding” is the appropriate term to use for this particular sort of feeling, that is not what we mean when we say that science provides understanding. We’re looking for more than just a good feeling because such feelings sometimes are misleading.

The sense of understanding that we’re interested in isn’t a matter of hedging, merely expressing knowledge of a simple fact, or experiencing a feeling that a scientific explanation is correct. The sense of understanding that matters for our purposes and is the aim of science is a kind of cognitive success. It involves really grasping how the world is – not how we want it to be or what would make us feel good. When we genuinely understand some phenomenon, we are in an epistemic state that may or may not be accompanied by the phenomenal feeling of “aha.” Like almost everything else that philosophers study, there are numerous minute differences when it comes to accounts of understanding (and many differences that are not so minute). This shouldn’t dissuade us in our discussion though. After all, philosophers have been arguing about the exact nature of knowledge for many years (and they still don’t seem to be anywhere near a consensus), but this hasn’t hindered our general ability to recognize whether something is known or not.

While there is live debate among philosophers about whether understanding is itself a kind of knowledge, it is generally agreed that understanding is something beyond mere knowledge of facts. To illustrate the plausibility of this idea, consider a student who simply parrots what their biology teacher tells them. This student knows, for example, that the DNA sequences of humans and chimpanzees are extraordinarily similar because the teacher has said this. However, the student may be clueless as to why this is so, or the importance of this similarity within the larger context of biology. The student may have no grasp of how this fact provides support for evolution in general and common descent in particular. What is missing? It seems that whereas the student simply knows this (and perhaps many other) isolated facts, understanding requires seeing how these facts hang together. The person with understanding grasps biology (or any other object of understanding) as an interrelated body of information with many connections between the various facts. This person can appreciate how these various facts depend upon one another. Furthermore, the person who understands biology can use this understanding to explain and sometimes predict particular biological phenomena.

Following philosopher of science Henk de Regt, we can helpfully distinguish between two varieties of understanding. The first is what we’ve been primarily considering until now: understanding phenomena. We achieve this sort of understanding by coming to know why or how a particular phenomenon occurs. In other words, we come to understand phenomena when we grasp correct scientific explanations of what causes the phenomena, the mechanisms or processes involved in the production of the phenomena, how the phenomena fall under natural laws, and/or how various changes to other things might have led to changes in the phenomena. A classic example of this is the understanding of the variation among the finches of the Galapagos Islands. As Charles Darwin noted, we can explain this phenomenon (variation among the different kinds of finches on these islands) by recognizing that it was the result of adaptation by natural selection. Recognizing the process (natural selection) and the causes (differences in environmental conditions, variation in characteristics such as beak size within populations) that led to the diversification of the finches yields understanding of the phenomena. This sort of understanding is the primary aim of science.

The second sort of understanding that we are concerned with is understanding a theory. One can genuinely understand phenomena only if one understands the relevant scientific theories. What exactly does it mean to understand a scientific theory though? And, what do we even mean by calling something a scientific “theory”? Let’s start with the second question. We don’t mean by “theory” the sort of thing that is far too often meant by it in public discourse. In such cases “theory” is often used to signify a claim or hypothesis that is still the subject of significant, reasonable doubt. It is exactly this sort of use of “theory” that is operative when critics erroneously charge that evolution is just a theory . Instead, when we speak of theories in science, we are talking about well-established domains of science that enjoy strong empirical support and include many widely accepted foundational facts, methods, and laws or principles. When it comes to understanding a theory this consists of being able to use the scientific theory to construct (or at least appreciate) scientific explanations or make predictions about phenomena within a particular domain. For instance, someone who understands evolutionary theory can construct scientific explanations of a number of things such as the variation that one finds among the different species of finches in the Galapagos Islands; or make predictions about where specific fossils could be found, as in the case of Tiktaalik , an extinct lobe-finned fish that has many similarities with four-limbed animals.

Possessing understanding of a scientific theory will depend on various factors. Some of those factors have to do with the scientific theory itself – the simplicity of its structure, its facility to be utilized for predictions, and so on. Other factors will depend upon the individual – things like background knowledge and intellectual capacity are key factors when it comes to whether someone is able to come to understand a scientific theory. Of course, understanding is something that comes in degrees. So, one person might have a deeper understanding of a scientific theory (or phenomenon) than another. An easy way to see this is to consider the different levels of understanding that an expert and an informed layperson may have when it comes to particular scientific theories. An expert can generate a new evolutionary explanation of some disease, such as COVID-19, which suggests that the virus SARS-CoV-2 likely evolved naturally rather than being designed in a lab. A layperson can appreciate this explanation when hearing of it, but typically the layperson couldn’t come up with this explanation on their own. In such a case, both the expert and the layperson are exhibiting some degree of understanding of theories of viral evolution. However, the expert is exhibiting a significantly higher degree of understanding of the scientific theories in question, and plausibly as a result of this the expert has a deeper understanding of the phenomenon that the scientific theories are being employed to explain – for instance, how the sequences of the genomes of the various viruses can be compared and how such comparisons can form the basis for estimating evolutionary relations.

It is worth briefly pausing to emphasize the importance of distinguishing between understanding phenomena and understanding theories. Two primary reasons this is important to do are that this distinction helps us better appreciate how science achieves understanding and why such understanding really is an achievement . As we have discussed, coming to understand phenomena requires exercising one’s cognitive faculties, in particular one’s understanding of scientific theories, to generate or appreciate scientific explanations of the phenomena in question. For genuine understanding of a phenomenon, it is not enough that one is simply informed of a scientific explanation; one must appreciate how the scientific explanation provides an account of why or how the phenomenon occurs. At the heart of this process lies scientific explanations – generating them, comparing them, or at the very least appreciating them. The central cognitive aim of science cannot be had without scientific explanations.

Key Successes of Scientific Explanation

The history of science is replete with examples of successful scientific explanations. Often, as we discuss in later chapters, these scientific explanations lead to significant new discoveries. In other cases, they provide deep understanding of phenomena that were previously mysterious. And, in many other cases, they help aid in controlling various phenomena (such as infections) and developing new technologies. Arguably a big part of the reason that science advanced so quickly after the scientific revolution began is that key scientific explanations were hit upon. That said, let’s take a brief look at two of the incredible successes of scientific explanation (we’ll consider others in later chapters).

In the early 1800s, it was discovered that the orbit of Uranus (at that time believed to be the last planet in our solar system) didn’t follow the path predicted by Newton’s theories, coupled with the assumption that there were no other planets. What was to be made of this? Since the empirical evidence was undeniable, there were only two options. Either give up Newton’s theory of universal gravitation or abandon the assumption that there were no other planets beyond Uranus in our solar system. At that time, especially given its tremendous successes, dropping Newton’s theory wasn’t appealing. Two scientists, John Couch Adams and Urbain Leverrier, working independently of one another, hit upon a better explanation. They postulated that there was a thus far undiscovered planet that was causing the orbit of Uranus to be different than expected. This explanation accounted for the strangeness of Uranus’ orbit without abandoning Newton’s theory. The great success of this scientific explanation came shortly after it was put forward, when the existence of Neptune was observationally confirmed.

Another great success of explanation in science was the line of reasoning that led Charles Darwin to the theory of natural selection. As he described in The Origin of the Species : “It can hardly be supposed that a false theory would explain, in so satisfactory a manner as does the theory of natural selection, the several large classes of facts above specified. It has recently been objected that this is an unsafe method of arguing; but it is a method used in judging of the common events of life and has often been used by the greatest natural philosophers.” The explanatory success of evolutionary theory in the life sciences can hardly be overstated. As evolutionary biologist Theodosius Dobzhansky once said, “Nothing in biology makes sense except in the light of evolution.” The understanding of various phenomena that has resulted from understanding evolutionary theory is nothing short of astounding.

There are innumerable other instances where scientific explanations have been tremendously successful – the oxygen theory of combustion, the discovery of electrons because of the explanations their existence provides, and many others. Scientific explanations have been tremendously successful as the method of achieving the primary aim of science: understanding.

At this point one might be inclined to wonder: What is the relationship between understanding and truth? Does science ever get to the absolute truth? Can we really understand without knowing the whole truth? Does science even aim at discovering the truth? These questions mark a good place to briefly pause to avoid a potential misunderstanding about how truth figures into the discussions in this book. While scientific knowledge and the depth of our understanding is always apt to change over time, this doesn’t mean that science doesn’t seek truth – it does. The sense in which scientific knowledge changes over time is that we often learn that what we thought was knowledge wasn’t genuine knowledge. When we speak of the current state of scientific knowledge or our depth of scientific understanding, we are speaking of what we have good evidence to think is actually the truth. However, because that evidence is never sufficient for absolute certainty, our judgment is always open to revision, that is, our judgment of whether something is the absolute truth and whether we fully understand something is tentative.

What’s to Come

In this chapter we’ve properly begun our investigation into how science explains the world. Predominantly, our discussion has so far centered on why science explains the world. As we have seen, the reason for this is fairly simple. Science explains the world because it is by way of scientific explanations that the chief cognitive aim of science is achieved. We only come to scientific understanding of the physical world by possessing scientific explanations. In the remainder of the book, we turn our attention to how science explains the world. Here’s a brief preview of what’s to come and the many questions we seek to answer.

In Chapter 2 , we discuss the relationship between scientific explanations and ordinary, everyday explanations. How are they similar? How are they different?

We turn our attention to specific kinds of scientific explanations in Chapter 3 . How are scientific explanations that describe what actually happens related to scientific explanations concerning what might or could happen in other situations? Can historical explanations be genuinely scientific? If historical explanations can be genuinely scientific, are they, nevertheless, inferior to experimental scientific explanations? Do even the best scientific explanations (whether experimental or historical) explain everything and leave no uncertainties?

Our focus in Chapter 4 is the relationship between scientific explanation and prediction. Are they the same thing, or at least symmetrical? Can good scientific explanations fail to make accurate predictions? Can scientific models that provide accurate predictions fail to offer good scientific explanations?

We explore how we evaluate the quality of scientific explanations in Chapter 5 . What sort of features are theoretical virtues? Do these virtues make one scientific explanation better than another? Granting that particular theoretical virtues do make some scientific explanations better than others, are we any good at evaluating which scientific explanations actually have these virtues? What if theoretical virtues are simply things that make models or theories convenient for us to use? In other words, should we think that theoretical virtues are really guides to the truth?

In Chapter 6 , we delve more deeply into the relationship between scientific explanations and the understanding that they generate. What is understanding in general? How does one come to have scientific understanding? How is it related to the experience one gets when it feels as if one understands?

Next, in Chapter 7 , we examine the role that idealizations play in generating scientific explanations. What exactly are idealizations? Why do we use them in science? Can idealizations be a means of generating scientific understanding?

We conclude the main part of our discussion, in Chapter 8 , by looking at how scientific explanations generate scientific knowledge. Scientific explanations generate scientific knowledge via inference to the best explanation. But, what exactly is this method of inference? When do we employ inference to the best explanation? How does this inference allow scientific explanations to generate scientific knowledge? Is inference to the best explanation even a legitimate form of reasoning? If a model or theory is to be “real” science, must it not only be the best explanation but also explain everything in a given domain?

In answering the above questions our discussion covers a lot of ground from the nature of scientific explanation to how scientific explanations are used to generate scientific understanding and scientific knowledge to the role of idealizations in producing scientific explanations. We take our time working through these issues so that we can harvest genuine insights. That said, we also avoid getting bogged down in details that are unnecessary for our purposes. By the end of our journey, a clear picture of the role of explanation in science emerges. Along the way we consider, and clear up, various common misunderstandings of these issues (these are summarized at the end of the book). Now let’s continue on our path to discovering how science explains the world.

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

• Why Explanation Matters in Science
• Kevin McCain , University of Alabama, Birmingham
• Book: Understanding How Science Explains the World
• Online publication: 17 June 2022
• Chapter DOI: https://doi.org/10.1017/9781108997027.003

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive .

It’s a wonderful world — and universe — out there.

Come explore with us!  

Science News Explores

Scientists say: hypothesis.

This is an idea put forth to explain events in the natural world

In the process of making discoveries, scientists make hypotheses. These are ideas put forth to explain things in the natural world that scientists then investigate through experiments, observations and other methods.   

Django/E+/Getty Images

Share this:

• Google Classroom

By Carolyn Wilke

November 4, 2019 at 6:30 am

Hypothesis (noun, “Hi-PAH-theh-sis”)

This is an idea that may explain phenomena in the natural world. Making a hypothesis is one part of the process scientists use to make new discoveries. Before making a hypothesis, scientists may read about a topic to understand it better. They may talk with other scientists about it. Then they ask questions about things they don’t yet understand. The answers they propose are their hypotheses (“Hi-PAH-theh-SEAS”). Until confirmed, or disproven, such answers may be described as hypothetical ones.

For example, a team of biologists was studying how whales communicate. They knew that mother and baby humpback whales call quietly to each other when they migrate. So they formed a hypothesis that other kinds of whales also communicate quietly. To test this idea, researchers listened in on another species. They placed audio, or sound, recorders on mama southern right whales. The sound data they recorded showed that right whales whisper , too.

Other researchers use computer models to investigate their hypotheses. Models are very helpful when studying faraway things, like stars and planets. Some astronomers, for example, wonder if other planets have liquid water. Scientists have recently learned that a faraway planet called K2 18b has water vapor , the stuff clouds are made of. Could it also rain on that planet? To find out, researchers developed a computer model that simulated conditions on that planet.

In a sentence

Seeing that some hummingbirds have sharp or hooked bills led scientists to form the hypothesis that some birds use their bills as weapons .

Check out the full list of Scientists Say .

1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/concepts-biology/pages/1-introduction

• Authors: Samantha Fowler, Rebecca Roush, James Wise
• Publisher/website: OpenStax
• Book title: Concepts of Biology
• Publication date: Apr 25, 2013
• Location: Houston, Texas
• Book URL: https://openstax.org/books/concepts-biology/pages/1-introduction
• Section URL: https://openstax.org/books/concepts-biology/pages/1-2-the-process-of-science

© Jul 10, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

• Table of Contents
• Random Entry
• Chronological
• Editorial Information
• About the SEP
• Editorial Board
• How to Cite the SEP
• Special Characters
• Advanced Tools
• Support the SEP
• PDFs for SEP Friends
• Make a Donation
• SEPIA for Libraries
• Entry Contents

Bibliography

Academic tools.

• Friends PDF Preview
• Author and Citation Info
• Back to Top

Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

• Aikenhead, G.S., 1987, “High-school graduates’ beliefs about science-technology-society. III. Characteristics and limitations of scientific knowledge”, Science Education , 71(4): 459–487.
• Allchin, D., H.M. Andersen and K. Nielsen, 2014, “Complementary Approaches to Teaching Nature of Science: Integrating Student Inquiry, Historical Cases, and Contemporary Cases in Classroom Practice”, Science Education , 98: 461–486.
• Anderson, C., 2008, “The end of theory: The data deluge makes the scientific method obsolete”, Wired magazine , 16(7): 16–07
• Arabatzis, T., 2006, “On the inextricability of the context of discovery and the context of justification”, in Revisiting Discovery and Justification , J. Schickore and F. Steinle (eds.), Dordrecht: Springer, pp. 215–230.
• Barnes, J. (ed.), 1984, The Complete Works of Aristotle, Vols I and II , Princeton: Princeton University Press.
• Barnes, B. and D. Bloor, 1982, “Relativism, Rationalism, and the Sociology of Knowledge”, in Rationality and Relativism , M. Hollis and S. Lukes (eds.), Cambridge: MIT Press, pp. 1–20.
• Bauer, H.H., 1992, Scientific Literacy and the Myth of the Scientific Method , Urbana: University of Illinois Press.
• Bechtel, W. and R.C. Richardson, 1993, Discovering complexity , Princeton, NJ: Princeton University Press.
• Berkeley, G., 1734, The Analyst in De Motu and The Analyst: A Modern Edition with Introductions and Commentary , D. Jesseph (trans. and ed.), Dordrecht: Kluwer Academic Publishers, 1992.
• Blachowicz, J., 2009, “How science textbooks treat scientific method: A philosopher’s perspective”, The British Journal for the Philosophy of Science , 60(2): 303–344.
• Bloor, D., 1991, Knowledge and Social Imagery , Chicago: University of Chicago Press, 2 nd edition.
• Boyle, R., 1682, New experiments physico-mechanical, touching the air , Printed by Miles Flesher for Richard Davis, bookseller in Oxford.
• Bridgman, P.W., 1927, The Logic of Modern Physics , New York: Macmillan.
• –––, 1956, “The Methodological Character of Theoretical Concepts”, in The Foundations of Science and the Concepts of Science and Psychology , Herbert Feigl and Michael Scriven (eds.), Minnesota: University of Minneapolis Press, pp. 38–76.
• Burian, R., 1997, “Exploratory Experimentation and the Role of Histochemical Techniques in the Work of Jean Brachet, 1938–1952”, History and Philosophy of the Life Sciences , 19(1): 27–45.
• –––, 2007, “On microRNA and the need for exploratory experimentation in post-genomic molecular biology”, History and Philosophy of the Life Sciences , 29(3): 285–311.
• Carnap, R., 1928, Der logische Aufbau der Welt , Berlin: Bernary, transl. by R.A. George, The Logical Structure of the World , Berkeley: University of California Press, 1967.
• –––, 1956, “The methodological character of theoretical concepts”, Minnesota studies in the philosophy of science , 1: 38–76.
• Carrol, S., and D. Goodstein, 2009, “Defining the scientific method”, Nature Methods , 6: 237.
• Churchman, C.W., 1948, “Science, Pragmatics, Induction”, Philosophy of Science , 15(3): 249–268.
• Cooper, J. (ed.), 1997, Plato: Complete Works , Indianapolis: Hackett.
• Darden, L., 1991, Theory Change in Science: Strategies from Mendelian Genetics , Oxford: Oxford University Press
• Dewey, J., 1910, How we think , New York: Dover Publications (reprinted 1997).
• Douglas, H., 2009, Science, Policy, and the Value-Free Ideal , Pittsburgh: University of Pittsburgh Press.
• Dupré, J., 2004, “Miracle of Monism ”, in Naturalism in Question , Mario De Caro and David Macarthur (eds.), Cambridge, MA: Harvard University Press, pp. 36–58.
• Elliott, K.C., 2007, “Varieties of exploratory experimentation in nanotoxicology”, History and Philosophy of the Life Sciences , 29(3): 311–334.
• Elliott, K. C., and T. Richards (eds.), 2017, Exploring inductive risk: Case studies of values in science , Oxford: Oxford University Press.
• Falcon, Andrea, 2005, Aristotle and the science of nature: Unity without uniformity , Cambridge: Cambridge University Press.
• Feyerabend, P., 1978, Science in a Free Society , London: New Left Books
• –––, 1988, Against Method , London: Verso, 2 nd edition.
• Fisher, R.A., 1955, “Statistical Methods and Scientific Induction”, Journal of The Royal Statistical Society. Series B (Methodological) , 17(1): 69–78.
• Foster, K. and P.W. Huber, 1999, Judging Science. Scientific Knowledge and the Federal Courts , Cambridge: MIT Press.
• Fox Keller, E., 2003, “Models, Simulation, and ‘computer experiments’”, in The Philosophy of Scientific Experimentation , H. Radder (ed.), Pittsburgh: Pittsburgh University Press, 198–215.
• Gilbert, G., 1976, “The transformation of research findings into scientific knowledge”, Social Studies of Science , 6: 281–306.
• Gimbel, S., 2011, Exploring the Scientific Method , Chicago: University of Chicago Press.
• Goodman, N., 1965, Fact , Fiction, and Forecast , Indianapolis: Bobbs-Merrill.
• Haack, S., 1995, “Science is neither sacred nor a confidence trick”, Foundations of Science , 1(3): 323–335.
• –––, 2003, Defending science—within reason , Amherst: Prometheus.
• –––, 2005a, “Disentangling Daubert: an epistemological study in theory and practice”, Journal of Philosophy, Science and Law , 5, Haack 2005a available online . doi:10.5840/jpsl2005513
• –––, 2005b, “Trial and error: The Supreme Court’s philosophy of science”, American Journal of Public Health , 95: S66-S73.
• –––, 2010, “Federal Philosophy of Science: A Deconstruction-and a Reconstruction”, NYUJL & Liberty , 5: 394.
• Hangel, N. and J. Schickore, 2017, “Scientists’ conceptions of good research practice”, Perspectives on Science , 25(6): 766–791
• Harper, W.L., 2011, Isaac Newton’s Scientific Method: Turning Data into Evidence about Gravity and Cosmology , Oxford: Oxford University Press.
• Hempel, C., 1950, “Problems and Changes in the Empiricist Criterion of Meaning”, Revue Internationale de Philosophie , 41(11): 41–63.
• –––, 1951, “The Concept of Cognitive Significance: A Reconsideration”, Proceedings of the American Academy of Arts and Sciences , 80(1): 61–77.
• –––, 1965, Aspects of scientific explanation and other essays in the philosophy of science , New York–London: Free Press.
• –––, 1966, Philosophy of Natural Science , Englewood Cliffs: Prentice-Hall.
• Holmes, F.L., 1987, “Scientific writing and scientific discovery”, Isis , 78(2): 220–235.
• Howard, D., 2003, “Two left turns make a right: On the curious political career of North American philosophy of science at midcentury”, in Logical Empiricism in North America , G.L. Hardcastle & A.W. Richardson (eds.), Minneapolis: University of Minnesota Press, pp. 25–93.
• Hoyningen-Huene, P., 2008, “Systematicity: The nature of science”, Philosophia , 36(2): 167–180.
• –––, 2013, Systematicity. The Nature of Science , Oxford: Oxford University Press.
• Howie, D., 2002, Interpreting probability: Controversies and developments in the early twentieth century , Cambridge: Cambridge University Press.
• Hughes, R., 1999, “The Ising Model, Computer Simulation, and Universal Physics”, in Models as Mediators , M. Morgan and M. Morrison (eds.), Cambridge: Cambridge University Press, pp. 97–145
• Hume, D., 1739, A Treatise of Human Nature , D. Fate Norton and M.J. Norton (eds.), Oxford: Oxford University Press, 2000.
• Humphreys, P., 1995, “Computational science and scientific method”, Minds and Machines , 5(1): 499–512.
• ICMJE, 2013, “Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals”, International Committee of Medical Journal Editors, available online , accessed August 13 2014
• Jeffrey, R.C., 1956, “Valuation and Acceptance of Scientific Hypotheses”, Philosophy of Science , 23(3): 237–246.
• Kaufmann, W.J., and L.L. Smarr, 1993, Supercomputing and the Transformation of Science , New York: Scientific American Library.
• Knorr-Cetina, K., 1981, The Manufacture of Knowledge , Oxford: Pergamon Press.
• Krohs, U., 2012, “Convenience experimentation”, Studies in History and Philosophy of Biological and BiomedicalSciences , 43: 52–57.
• Kuhn, T.S., 1962, The Structure of Scientific Revolutions , Chicago: University of Chicago Press
• Latour, B. and S. Woolgar, 1986, Laboratory Life: The Construction of Scientific Facts , Princeton: Princeton University Press, 2 nd edition.
• Laudan, L., 1968, “Theories of scientific method from Plato to Mach”, History of Science , 7(1): 1–63.
• Lenhard, J., 2006, “Models and statistical inference: The controversy between Fisher and Neyman-Pearson”, The British Journal for the Philosophy of Science , 57(1): 69–91.
• Leonelli, S., 2012, “Making Sense of Data-Driven Research in the Biological and the Biomedical Sciences”, Studies in the History and Philosophy of the Biological and Biomedical Sciences , 43(1): 1–3.
• Levi, I., 1960, “Must the scientist make value judgments?”, Philosophy of Science , 57(11): 345–357
• Lindley, D., 1991, Theory Change in Science: Strategies from Mendelian Genetics , Oxford: Oxford University Press.
• Lipton, P., 2004, Inference to the Best Explanation , London: Routledge, 2 nd edition.
• Marks, H.M., 2000, The progress of experiment: science and therapeutic reform in the United States, 1900–1990 , Cambridge: Cambridge University Press.
• Mazzochi, F., 2015, “Could Big Data be the end of theory in science?”, EMBO reports , 16: 1250–1255.
• Mayo, D.G., 1996, Error and the Growth of Experimental Knowledge , Chicago: University of Chicago Press.
• McComas, W.F., 1996, “Ten myths of science: Reexamining what we think we know about the nature of science”, School Science and Mathematics , 96(1): 10–16.
• Medawar, P.B., 1963/1996, “Is the scientific paper a fraud”, in The Strange Case of the Spotted Mouse and Other Classic Essays on Science , Oxford: Oxford University Press, 33–39.
• Mill, J.S., 1963, Collected Works of John Stuart Mill , J. M. Robson (ed.), Toronto: University of Toronto Press
• NAS, 1992, Responsible Science: Ensuring the integrity of the research process , Washington DC: National Academy Press.
• Nersessian, N.J., 1987, “A cognitive-historical approach to meaning in scientific theories”, in The process of science , N. Nersessian (ed.), Berlin: Springer, pp. 161–177.
• –––, 2008, Creating Scientific Concepts , Cambridge: MIT Press.
• Newton, I., 1726, Philosophiae naturalis Principia Mathematica (3 rd edition), in The Principia: Mathematical Principles of Natural Philosophy: A New Translation , I.B. Cohen and A. Whitman (trans.), Berkeley: University of California Press, 1999.
• –––, 1704, Opticks or A Treatise of the Reflections, Refractions, Inflections & Colors of Light , New York: Dover Publications, 1952.
• Neyman, J., 1956, “Note on an Article by Sir Ronald Fisher”, Journal of the Royal Statistical Society. Series B (Methodological) , 18: 288–294.
• Nickles, T., 1987, “Methodology, heuristics, and rationality”, in Rational changes in science: Essays on Scientific Reasoning , J.C. Pitt (ed.), Berlin: Springer, pp. 103–132.
• Nicod, J., 1924, Le problème logique de l’induction , Paris: Alcan. (Engl. transl. “The Logical Problem of Induction”, in Foundations of Geometry and Induction , London: Routledge, 2000.)
• Nola, R. and H. Sankey, 2000a, “A selective survey of theories of scientific method”, in Nola and Sankey 2000b: 1–65.
• –––, 2000b, After Popper, Kuhn and Feyerabend. Recent Issues in Theories of Scientific Method , London: Springer.
• –––, 2007, Theories of Scientific Method , Stocksfield: Acumen.
• Norton, S., and F. Suppe, 2001, “Why atmospheric modeling is good science”, in Changing the Atmosphere: Expert Knowledge and Environmental Governance , C. Miller and P. Edwards (eds.), Cambridge, MA: MIT Press, 88–133.
• O’Malley, M., 2007, “Exploratory experimentation and scientific practice: Metagenomics and the proteorhodopsin case”, History and Philosophy of the Life Sciences , 29(3): 337–360.
• O’Malley, M., C. Haufe, K. Elliot, and R. Burian, 2009, “Philosophies of Funding”, Cell , 138: 611–615.
• Oreskes, N., K. Shrader-Frechette, and K. Belitz, 1994, “Verification, Validation and Confirmation of Numerical Models in the Earth Sciences”, Science , 263(5147): 641–646.
• Osborne, J., S. Simon, and S. Collins, 2003, “Attitudes towards science: a review of the literature and its implications”, International Journal of Science Education , 25(9): 1049–1079.
• Parascandola, M., 1998, “Epidemiology—2 nd -Rate Science”, Public Health Reports , 113(4): 312–320.
• Parker, W., 2008a, “Franklin, Holmes and the Epistemology of Computer Simulation”, International Studies in the Philosophy of Science , 22(2): 165–83.
• –––, 2008b, “Computer Simulation through an Error-Statistical Lens”, Synthese , 163(3): 371–84.
• Pearson, K. 1892, The Grammar of Science , London: J.M. Dents and Sons, 1951
• Pearson, E.S., 1955, “Statistical Concepts in Their Relation to Reality”, Journal of the Royal Statistical Society , B, 17: 204–207.
• Pickering, A., 1984, Constructing Quarks: A Sociological History of Particle Physics , Edinburgh: Edinburgh University Press.
• Popper, K.R., 1959, The Logic of Scientific Discovery , London: Routledge, 2002
• –––, 1963, Conjectures and Refutations , London: Routledge, 2002.
• –––, 1985, Unended Quest: An Intellectual Autobiography , La Salle: Open Court Publishing Co..
• Rudner, R., 1953, “The Scientist Qua Scientist Making Value Judgments”, Philosophy of Science , 20(1): 1–6.
• Rudolph, J.L., 2005, “Epistemology for the masses: The origin of ‘The Scientific Method’ in American Schools”, History of Education Quarterly , 45(3): 341–376
• Schickore, J., 2008, “Doing science, writing science”, Philosophy of Science , 75: 323–343.
• Schickore, J. and N. Hangel, 2019, “‘It might be this, it should be that…’ uncertainty and doubt in day-to-day science practice”, European Journal for Philosophy of Science , 9(2): 31. doi:10.1007/s13194-019-0253-9
• Shamoo, A.E. and D.B. Resnik, 2009, Responsible Conduct of Research , Oxford: Oxford University Press.
• Shank, J.B., 2008, The Newton Wars and the Beginning of the French Enlightenment , Chicago: The University of Chicago Press.
• Shapin, S. and S. Schaffer, 1985, Leviathan and the air-pump , Princeton: Princeton University Press.
• Smith, G.E., 2002, “The Methodology of the Principia”, in The Cambridge Companion to Newton , I.B. Cohen and G.E. Smith (eds.), Cambridge: Cambridge University Press, 138–173.
• Snyder, L.J., 1997a, “Discoverers’ Induction”, Philosophy of Science , 64: 580–604.
• –––, 1997b, “The Mill-Whewell Debate: Much Ado About Induction”, Perspectives on Science , 5: 159–198.
• –––, 1999, “Renovating the Novum Organum: Bacon, Whewell and Induction”, Studies in History and Philosophy of Science , 30: 531–557.
• Sober, E., 2008, Evidence and Evolution. The logic behind the science , Cambridge: Cambridge University Press
• Sprenger, J. and S. Hartmann, 2019, Bayesian philosophy of science , Oxford: Oxford University Press.
• Steinle, F., 1997, “Entering New Fields: Exploratory Uses of Experimentation”, Philosophy of Science (Proceedings), 64: S65–S74.
• –––, 2002, “Experiments in History and Philosophy of Science”, Perspectives on Science , 10(4): 408–432.
• Strasser, B.J., 2012, “Data-driven sciences: From wonder cabinets to electronic databases”, Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences , 43(1): 85–87.
• Succi, S. and P.V. Coveney, 2018, “Big data: the end of the scientific method?”, Philosophical Transactions of the Royal Society A , 377: 20180145. doi:10.1098/rsta.2018.0145
• Suppe, F., 1998, “The Structure of a Scientific Paper”, Philosophy of Science , 65(3): 381–405.
• Swijtink, Z.G., 1987, “The objectification of observation: Measurement and statistical methods in the nineteenth century”, in The probabilistic revolution. Ideas in History, Vol. 1 , L. Kruger (ed.), Cambridge MA: MIT Press, pp. 261–285.
• Waters, C.K., 2007, “The nature and context of exploratory experimentation: An introduction to three case studies of exploratory research”, History and Philosophy of the Life Sciences , 29(3): 275–284.
• Weinberg, S., 1995, “The methods of science… and those by which we live”, Academic Questions , 8(2): 7–13.
• Weissert, T., 1997, The Genesis of Simulation in Dynamics: Pursuing the Fermi-Pasta-Ulam Problem , New York: Springer Verlag.
• William H., 1628, Exercitatio Anatomica de Motu Cordis et Sanguinis in Animalibus , in On the Motion of the Heart and Blood in Animals , R. Willis (trans.), Buffalo: Prometheus Books, 1993.
• Winsberg, E., 2010, Science in the Age of Computer Simulation , Chicago: University of Chicago Press.
• Wivagg, D. & D. Allchin, 2002, “The Dogma of the Scientific Method”, The American Biology Teacher , 64(9): 645–646

How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

• Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
• Scientific Method at philpapers. Darrell Rowbottom (ed.).
• Recent Articles | Scientific Method | The Scientist Magazine

al-Kindi | Albert the Great [= Albertus magnus] | Aquinas, Thomas | Arabic and Islamic Philosophy, disciplines in: natural philosophy and natural science | Arabic and Islamic Philosophy, historical and methodological topics in: Greek sources | Arabic and Islamic Philosophy, historical and methodological topics in: influence of Arabic and Islamic Philosophy on the Latin West | Aristotle | Bacon, Francis | Bacon, Roger | Berkeley, George | biology: experiment in | Boyle, Robert | Cambridge Platonists | confirmation | Descartes, René | Enlightenment | epistemology | epistemology: Bayesian | epistemology: social | Feyerabend, Paul | Galileo Galilei | Grosseteste, Robert | Hempel, Carl | Hume, David | Hume, David: Newtonianism and Anti-Newtonianism | induction: problem of | Kant, Immanuel | Kuhn, Thomas | Leibniz, Gottfried Wilhelm | Locke, John | Mill, John Stuart | More, Henry | Neurath, Otto | Newton, Isaac | Newton, Isaac: philosophy | Ockham [Occam], William | operationalism | Peirce, Charles Sanders | Plato | Popper, Karl | rationality: historicist theories of | Reichenbach, Hans | reproducibility, scientific | Schlick, Moritz | science: and pseudo-science | science: theory and observation in | science: unity of | scientific discovery | scientific knowledge: social dimensions of | simulations in science | skepticism: medieval | space and time: absolute and relational space and motion, post-Newtonian theories | Vienna Circle | Whewell, William | Zabarella, Giacomo

Copyright © 2021 by Brian Hepburn < brian . hepburn @ wichita . edu > Hanne Andersen < hanne . andersen @ ind . ku . dk >

• Accessibility

Support SEP

Mirror sites.

View this site from another server:

• Info about mirror sites

The Stanford Encyclopedia of Philosophy is copyright © 2023 by The Metaphysics Research Lab , Department of Philosophy, Stanford University

Library of Congress Catalog Data: ISSN 1095-5054

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

1 Science and the Scientific Method

Science is with us everywhere. Advances in technology and science are rapidly transforming our world – from growing food, developing medicines, making exercise regimes, recycling and presenting the daily weather report, to reading a map and using our mobile phones and computers.

The modern world would not be modern at all without the understandings and technology enabled by science. In the future, being science literate will be a necessity, not an option. We cannot escape from the significance of science.

A student scientist is someone who is being formally educated about science and how to practise science, as well as developing scientific skills and attributes such as teamwork, communication, and personal and professional responsibility.

This chapter introduces the concept of science and its importance in our lives ( Sections 1.1 and 1.2 ). Student scientists will learn about what science is and how it works, and how science graduates are able to change society for the better ( Section 1.2 ). Section 1.3 warns the student scientist about pseudoscience, and how it differs from ‘real’ science – what you are here to learn.

1.1 How medical science has shaped our history

Medical science includes a diverse range of specialities including anatomy and physiology, biochemistry, pathology, haematology, microbiology, immunology, and pharmacology to name a few. Within these various fields of study, many advances have been made through scientific investigation to improve our understanding of human biology, the basis of disease, and the diagnosis and treatment of disease. While the number of advancements are too vast to cover in a single text, we can gain an appreciation of the impact of these scientific advancements by a look at examples within physiology, one of the medical science areas of study.

Physiology is a branch of biology that deals with the normal functions and activities of living organisms and their parts. Physiologists are interested in the mechanical, physical and biomechanical functions of humans or animals, and their organs and cells.

Understanding and appreciating the history of physiology provides a context for learning current physiology. It also highlights how scientific understanding can change, and how current beliefs may one day be obsolete. The timeline below presents some of the notable discoveries in physiology that have informed our understanding of the human body and treatment for many diseases. These scientists have all been awarded Nobel prizes in Physiology or Medicine for their discoveries.

Box 1.1: Timeline of physiology discoveries

The website PhysiologyInfo.org, sponsored by the American Physiological Society, presents a timeline of physiology that highlights important discoveries between 1822 and 2013. General physiology milestones are presented in addition to milestones in endocrinology and metabolism.

1.2 Current issues in medical science

Every day scientific advancements that significantly enhance our understanding of physiology and quality of life are revealed. The American Physiological Society has identified six ‘hot topics’ in the life sciences that are particularly important today:

• cancer biology
• cardiovascular biology
• exercise and metabolism
• inflammation and immunity
• the microbiome (previously known as the gut flora)
• stem cells and regeneration.

Box 1.2 highlights some recent breakthroughs in four of these six key themes.

Box 1.2: Recent breakthroughs in physiology

Click on the drop-downs to read more.

1.3 Science literacy

A science degree helps students develop science literacy, so that graduates can contribute to society positively. Experts including science academics, science students, employment groups and professional societies identified three elements of science literacy that a science student should understand by the time they graduate ( Figure 1.1 ).

Graduates with scientific literacy are well prepared to participate in decision-making and debate socio-scientific issues that will improve society.

The scientific method is the procedure of systematic observation, and forming, testing and modifying hypotheses.

In high school and university, we learn science mostly from textbooks, which often presents science as absolute facts that we have always known. What the textbook doesn’t make obvious is that these facts are only the best current information we have, and that these facts are the results of years of work by many scientists experimenting.

To fully understand science, we need to know how scientists conduct science and obtain facts.

Cutting-edge knowledge at the frontier of science is not as thoroughly tested as the well-tested and long-standing knowledge existing in contemporary textbooks. New data may not stand up to the test of time. New theories, ideas and possibilities can easily disappear as research progresses and data are generated. What might have been initially imagined by the physiologist as a ‘break-through’ discovery can easily end up as nothing more than a hypothesis without data to support it.

Student scientists should have the opportunity to appreciate the full scope of science during their undergraduate degrees, which includes the freedom to imagine new ideas, but with adequate educator support to actually take on the role of a scientist and test these ideas. This way, a student can learn about and understand science.

Knowledge of science concepts, or ‘what do we know?’

The first element of scientific literacy is the most obvious – knowledge in science or the collection of facts that science has produced. Student scientists spend a large part of their undergraduate degrees learning scientific facts in their discipline area – that is, they focus on ‘what we know’ and not on ‘how we came to know it’. Scientific facts are claims about the world that can be directly established by a careful, unprejudiced use of the senses, or repeated and verifiable observations.

Scientific knowledge tells us about the nature of the world beyond what we see on the surface. It tells us about our cells and the organelles within, the transmission of energy from light hitting the retina in the eye to the electrical energy travelling down the optic nerve, and even about the conditions that existed in the world long before humans were around to observe it.

Scientific theory

The ultimate goal of science is to understand the natural world in terms of scientific theories – that is, concepts based on what we learn from experimentation. In science, our understanding is constantly increasing and, as a result, our theories develop and change.

In science, the word ‘theory’ is reserved for a conceptual scheme supported by a large number of observations; Figure 1.2  shows five currently accepted scientific theories.

Scientific theories cannot be conclusively proved or disproved, and they change when evidence requires it. Once theories are proposed to explain the facts on a particular topic, they are rigorously and ruthlessly tested by observation and experiment. Theories that don’t stand up to observational and experimental tests are eliminated and replaced by new potential explanations that must also be tested. Science progresses through the process of trial and error, by educated guesses and dismissals – only the most likely theories survive (see Box 1.3 ). Theories can never be described as true – rather, they are the best available explanations that are an improvement on anything we have had before.

Box 1.3: Scientific hypothesis, testing and theory

Science starts with problems associated with explaining some behaviour of the world or universe in light of an existing theory. An example given by Chalmers (2013) is the observation that bats can fly well at night, even though they have small, weak eyes.

This observation is problematic in light of the theory that living creatures see with their eyes. A potential answer  will be proposed to explain the observation – that is, a hypothesis. The hypothesis will then be tested and eliminated, or be successful. Once a hypothesis has managed to withstand a wide range of rigorous tests, it may become a theory and the scientist will move on to another problem and begin the process again.

It can take a lot of evidence and time to change established theories. There are many examples in science where theories were believed even when there was a great deal of opposing evidence – as explored in Box 1.4.

Box 1.4: The difficulties in changing scientific theory

Widely accepted during the 18th and 19th centuries, the miasma theory of disease transmission explained the origin and spread of some epidemic diseases like cholera. According to the theory, these diseases were caused by miasma (Greek for pollution) – an ill-defined, poisonous vapour secreted by rotting organic matter, or a noxious form of ‘bad air’ ( Figure 1.3 ).

In 1546, Italian physician, poet, astronomer and geologist Girolamo Fracastoro first proposed a hypothesis that contradicted the miasma theory – that contagious diseases were caused and spread by transferrable seed-like bodies. Other scientists proposed similar hypotheses , but it wasn’t until the work of Louis Pasteur and Robert Koch in the late 1860s that the germ theory became widely accepted. Joseph Lister, who came to be known as the ‘father of modern surgery’, applied the advances made by Pasteur to the hospital environment and introduced sterilisation of equipment, bedding, gowns and wounds, and washing hands to prevent infection.

Lister faced opposition despite the positive results he was achieving.  It didn’t help that bacteria were too small to see – but eventually his aseptic techniques were adopted throughout Germany, the United States, France and, finally, Great Britain.

Sometimes scientists believe in a hypothesis so much that they may think they are observing things that are not there. The canals of Mars are a fascinating (although non-physiological) example of this.

In 1877, Italian astronomer Giovanni Schiaparelli reported the discovery of canals on Mars (Hetherington, 1976); many astronomers including Percival Lowell (1911) and Eugène Antioniadi (1930) also documented seeing the canals and drew detailed maps. Around 1895, these drawings led to Lowell concluding that the canals were an irrigation system developed by Martians. When more sophisticated telescopes and imaging technology were available, astronomers including Antioniadi proposed that these canals were an optical illusion caused by dust moving across the surface of Mars due to heavy winds (Gifford, 1964). This was supported by images obtained in the 1960s by unmanned NASA spacecraft.

The facts we learn about physiology as student scientists are the results of years of careful, slow work by teams of scientists. Their results – and therefore our knowledge – is constantly evolving. Textbook ‘facts’ may have been challenged, and may still be challenged and evolving.

This leads into the second element of scientific literacy: understanding how we came to know scientific facts and theories.

Knowledge of the scientific method, or ‘how did we come to know it?’

Scientific method refers to the body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. It is based on gathering observable, empirical and measurable evidence subject to specific principles of reasoning   (Newton, 1726/1999, pp 794-796)

The scientific method includes the elements shown in Figure 1.4.

In a speech to graduating students at the California Institute of Technology in 2016, Atul Gawande, a surgeon and public health researcher, said:

Science is not a major or a career. It is a commitment to a systematic way of thinking, an allegiance to a way of building knowledge and explaining the universe through testing and factual observation (Gawande, 2016).

The process of making new discoveries in science is not as straightforward and neat as it may first appear. As a student scientist, you read textbooks, and conduct ‘cookbook’ practicals in which you follow a series of steps. In practice, creating new knowledge in science is characterised by difficulties, uncertainties and competing hypotheses

Science is a huge discipline, and the way science is done depends on the field of study. Exactly how a scientist conducts their craft depends on what knowledge and answers they are pursuing. For example, astronomers and geologists base their understanding on field observations, whereas physiologists and chemists base their understanding on experiments.

To a physiologist, the scientific method means implementing a series of experimental steps to create new knowledge and develop a more sophisticated understanding of a particular topic.

The scientific method follows seven general steps (see Figure 1.5 ):

• make an observation
• research what is already known about the topic (literature review)
• form a testable hypothesis that may explain the observation
• perform an experiment or series of experiments
• analyse the results
• accept or dismiss the hypothesis (conclusion)
• share the results.

Some philosophers and scientists prefer to see the scientific method as an ideal rather than a rule or a description of the practice of all scientists. Scientists are humans, and this means that we are not perfect and we are different in terms of our motivation, drive and ideas – all of which may result in science being practised differently.

Scientists may approach their quest for new discoveries differently, but they will always seek evidence that can be obtained in different ways. The scientific method combines rational thought and imagination to predict and explain phenomena, and the work of scientists is always open to scrutiny, criticism and debate.

Figure 1.6 shows a cyclic model of the scientific method, indicating how it is an ongoing process.

Video 1.1: Video overview of the scientific method [7 mins, 32 sec]

TED-Ed presentation by Adam Savage on two spectacular examples of profound scientific discoveries that came from simple, creative methods that anyone could have followed.

Note: Closed captions are available by selecting the CC button below.

Observation and research

Scientific research starts with a scientist making an initial observation that they cannot explain with an existing theory. It can also be a more open-ended questions, such as ‘How can I treat this particular disease?’ The scientist will study any previous data – that is, review the existing literature on the topic area, which are facts relevant to the problem at hand. This research may reveal that another scientist has already answered the question and, in this case, a new question will be asked. If the question hasn’t been answered, the scientist will use the findings of their research to help them to design a good question that they can test.

Hypothesis and aim

Research, and imaginative and creative thinking help the scientist to formulate a hypothesis that they can test to explain an observation or answer a question. A hypothesis is an assumption based on knowledge obtained during the observation stage – it is an ‘educated guess’.

Data generated during an experiment either support or fail to support the hypotheses. Hypotheses may be specific (e.g. chronic ingestion of artificial sweeteners causes weight gain in humans) or broad (e.g. viruses cause disease in animals). Hypotheses are always subject to modification.

It is important to note that hypotheses, however, may be proven untrue. This occurs when the data do not support the hypothesis, in which case, the hypothesis must be rejected or refined.

A hypothesis is tightly linked to the aim of the study, which is the objective or goal of an experiment or series of experiments. The aim is written in a similar manner to the hypothesis. A hypothesis is a prediction about the outcome of an experiment; an aim is usually written in the form of an instruction:

Hypothesis :  chronic ingestion of artificial sweetener causes weight gain in humans.

Aim: To determine if chronic  ingestion of artificial sweetener causes weight gain in humans.

Physiologists conduct experiments to test whether the real world behaves as predicted by the hypothesis. The purpose of the experiments is to find out if the resulting observations of the real world agree or conflict with the prediction – if they agree, we can be more confident that the hypothesis is true, but if they disagree we become less confident.

Experiments are a valuable scientific tool, because when experimenting we are able to control and manipulate phenomena and events. However, this also means that we are creating an ‘artificial environment’ for our experiments. This may not end up reflecting what is happening in the real – messy – world.

Experiments will only help us to support or not support hypothesis if they are carried out properly. This means using care and the right equipment and settings, and the with goal of minimising error. If we conduct careless experiments that introduce error into the results, the observations (or data) are useless when we try to evaluate our hypothesis.

Part of understanding how science works includes becoming aware of the methods and tools of science. Each scientific discipline has its own specialised techniques and tools that it uses to observe the natural world; student scientists will be exposed to some of these methods in practical classes during their studies. Experimental methods are not perfect, even when scientists try their best, and they can be updated or replaced over time. Advances in technology or scientific understanding may mean that previously accepted methods are rejected and replaced with new ones. This may also mean that experimental results obtained using old methods are also ignored, or viewed as less relevant, because our way of thinking about the topic has changed.

It is not always easy and straightforward to conduct well-designed experiments and to get reliable results. New experiments may take months or even years to perfect. Scientists need to work hard to collect high-quality results, including:

• developing their theoretical and practical knowledge of an area and a technique
• learning through trial and error
• mastering the available technology.

Researchers must provide details on how their experiments were conducted, because other researchers must be able to repeat the results. If results cannot be repeated, the hypothesis is no longer supported, and the conclusion may be incorrect. When the results of experiments are shared with the wider scientific community, the research will be judged on many things, including the methods employed.

Also, it is very rare for a researcher to generate their experimental methods from scratch; it is more common to use methods that have been shown by other researchers to be successful at measuring particular variables. This is another reason it is important to provide details of your methodology.

Scientists collect the data generated from their experiments and analyse them to determine whether the hypothesis was supported or not. In the analysis stage, the scientist combines the results from repeated experiments and carries out statistical analysis to test the hypothesis – for example, to determine whether differences exist between different experimental groups. Scientists will present their results as text, tables and graphs.

Results should be reproducible – that is, another scientist should be able to follow the same method and obtain the same results. If they are not able to do this, it may mean that the original results were an error. For this reason, scientists will usually conduct a single experiment multiple times.

Scientists rarely attempt to replicate the findings of other scientists, despite the importance of reproducibility. There are few incentives for scientists to dedicate time to replicating existing studies, and it can be difficult to replicate studies because the methods may be unclear, and poor or incorrect study designs may have been used in the original research (Belluz et al., 2016).

Recently, some scientists have found that when they do try to replicate studies, they have not been able to obtain the same results. This has led to a ‘crisis of reproducibility’. Scientists who have investigated reproducibility have found that few findings published in top psychology journals as well as highly cited papers on psychiatric treatment (Tajika et al., 2015) could be replicated (Open Science Collaboration, 2015). This issue is gaining more attention and must be addressed by scientists from all disciplines.

Conclusions are made about the results of the experiment in light of the hypothesis. Simply, the results do or do not support the hypothesis. The conclusions below are the two possibilities arising from the example hypothesis and aim shown previously:

Conclusion : Chronic  ingestion of artificial sweetener caused weight gain in humans.

Conclusion : Chronic ingestion of artificial sweetener did not cause weight gain in humans.

The study hypothesis, aim and conclusion are therefore highly connected.

Scientific conclusions are subject to change when new findings dictate that they need to be changed. Quite often in science, new studies, which might use new techniques and equipment, tell us when conclusions need to be modified or changed entirely.

Share results

Results of experiments should be shared with the wider scientific community, so they can contribute to the pool of knowledge on a topic and lead to better understanding for everyone.

Scientists can communicate with their peers in different ways:

• make an oral presentation at a conference
• present a poster at a conference
• publish a journal article in a peer-reviewed academic journal (see Box 1.5 ).

Publication of results makes information widely available and the results will be used by others if they are seen as interesting – results not deemed interesting will fade from sight; sadly, this is the fate of most published articles.

Box 1.5: Peer-reviewed articles and academic journals

Results do not become a part of the pool of shared knowledge until they are published, but not all results achieve this outcome. Scientists must present their findings in the accepted format – that is, a journal article, and convince journal editors and referees that their work is interesting, sound and useful.

Once a journal editor accepts an article, it is subjected to peer review. This is where others scientists with expertise in the relevant field, usually anonymously, read through and check that the work is of sufficient quality and worthy of publication. The publication and peer-review process is at the mercy of people and therefore not perfect – error, bias and dishonesty is inherent. As a result, some research that should be published isn’t – and some research that shouldn’t be published is.

This is a stressful process because research scientists in universities and industry aim to publish their work in academic and professional journals. Their publication record is tremendously important to scientists because it is often used to judge the quality of their work. Ultimately, a researcher’s publication record determines whether they get or keep jobs, or gain funding to continue their work.

Interesting and important results will be cited by other scientists in the field who will test the results, and often modify and extend on the research. They may also report back if they find something wrong or inadequate in the research. Results that withstand scrutiny by peers and continue to be seen as interesting, useful and correct (or not obviously wrong) will make their way into secondary literature, that is review articles, monographs and graduate-level textbooks. If more time passes and the results are still interesting and correct, they will find their way into undergraduate textbooks, which have the widest reach and influence.

An example of the scientific method is shown in Box 1.6 .

The language used in presentations and journal articles is highly technical and will contain a lot of scientific jargon. A non-expert audience will struggle to understand the meaning of such publications. To communicate their findings to lay – or non-expert – audiences, scientists may talk about their work during, for example, a radio or television interview.

Chapter 6 and Chapter 7 discusses the ins and outs of writing technical publications, and Chapter 8 talks more about communicating science to the non-expert audience.

Box 1.6: The scientific method in action

Observation : A physiologist determines that available evidence indicates that say drug X – which is not yet approved by Australia’s Therapeutic Goods Administration – increases the metabolic rate, and may therefore be a promising weight loss drug.

Hypothesis : The physiologist hypothesises that ‘drug X will cause weight loss in adult rats’.

Aim : The physiologist plans a study, with the aim to ‘determine if drug X will cause weight loss in adult rats’.

Experiment : The physiologist randomly assigns 20 adult rats to a control and treatment group – 10 per group. The control group is administered a daily dose of the placebo and the treatment group is administered a daily dose of the drug for 4 months. The control group receives a placebo instead of a drug, and all other conditions are identical to the treatment group with the exception of the drug. They receive the same kind of food and water. Body weight is recorded daily.

Results: At the end of the 4-month treatment period, the physiologist performs a statistical analysis on the body weight data. The data reveal that there is a statistical difference between treatment groups, with the treatment rats weighing less than the control rats.

Conclusion: Drug X caused weight loss in adult rats.

Share results: The physiologist will share their work with peers through a poster or oral presentation at a conference, and/or a journal article.

Knowledge and appreciation of science in society, or ‘how can we contribute’?

Understanding how science influences society – and vice versa – is an important aspect of scientific literacy. Science graduates who are able to combine their scientific knowledge and how it works with an understanding of the role of science in society will be valuable citizens who can contribute effectively to debates and decision making about science-related public issues.

One of the roles of a science graduate is to uphold the reputation of science in society by explaining what it is and why it is important. Many members of the public misunderstand what science is, what it means if something is scientific, and how science and technology are related. Some scientists may also share these misunderstandings (Bauer, 1992).

Video 1.2: Science and Society [4 mins, 37 secs]

Watch this video interview with Steven Pinker, renowned experimental psychologist and Professor in the Department of Psychology at Harvard University, on the place of science in society.

Note: Closed captions are available by selecting the CC button below,

Many people already do respect science, and this has been exploited in some advertisements that tell us that a product has been ‘scientifically proven’ to be better, stronger, faster, more efficient or even sexier than its rivals. This is meant to make the product more attractive to consumers because it implies that the claims are well founded and perhaps beyond dispute (Chalmers, 2013).

On the other hand, some people do mistrust science, as evidenced by some views about climate change and childhood vaccinations. Although most scientists agree that climate change is occurring, some of the public still view it as an issue that is up for debate. And despite the evidence supporting the positive impact of immunisation on human health, some parents are reluctant to vaccinate their children.

Scientists are responsible for discoveries that have resulted in nuclear weapons and pollution, and – as a result – are distrusted by some members of the public. A study published in 2012 reviewed data collected from American citizens on their social attitudes from 1974 to 2010 (Gauchat, 2012). The author found that trust in science has not declined during this time period, except among people who described themselves as politically conservative and those who frequently attended church. Also, levels of trust in science varied a lot according to social class, ethnicity, gender, and region. This is not surprising, given the way science and scientists have treated some populations – for example, African–American men in the Tuskegee study (see Case Study 1.1 ).

Case Study 1.1: Tuskegee study and distrust of doctors among African–Americans

Marcella Alsan and Marianne Wanamaker published a working paper in the National Bureau of Economic Research in 2016 (Alsan & Wanamaker, 2016); the abstract summarises their study.

Alsan and Wanamaker’s research showed that the unethical study may have led to a distrust in doctors, which contributed to (~35%) African–American men avoiding the health care system and therefore dying earlier. This effect was stronger the closer the men lived to Macon County, Alabama, where the Tuskegee study took place.

Although science influences how society functions, it is also true that society influences which topics are chosen for research and government funding. In 2015, the Australian Government developed a set of Science and Research Priorities . This project was led by the former Chief Scientist, Professor Ian Chubb, and included consultation with researchers, industry leaders and government representatives, who together represent all citizens. The areas that have been chosen as priorities are those deemed to be of critical importance to Australia and will be given public funding. They are:

• Soil and water
• Cybersecurity
• Advanced manufacturing
• Environmental change

1.4 Pseudoscience

“Pseudoscience is the form of science without the substance.”    (Gawande, 2016)

Pseudoscience describes an idea, field, practice, body of knowledge or belief that is presented as being scientific, but does not follow or obey the scientific method, and is not consistent with the standards of scientific research.

In 2006, the National Science Foundation (in the United States) analysed survey data from the previous 20 years and found that many Americans have many pseudoscientific beliefs, including astrology, lucky numbers, the existence of unidentified flying objects (UFOs), extrasensory perception (ESP) and magnetic therapy (National Center for Science and Engineering Statistics, 2006). They found that belief in pseudoscience increased during the 1990s and early 2000s, but declined between 2001 and 2005. In 2012 (National Center for Science and Engineering Statistics, 2014) :

• 55% of Americans said astrology is ‘not at all scientific’
• 32% said they thought astrology was ‘sort of scientific’
• 10% said it was ‘very scientific’
• about 4% said they ‘did not know’.

These numbers have not changed dramatically since surveys began, indicating that there is much for science communicators to achieve. Other examples of pseudoscience include acupuncture, alchemy, astrology, homeopathy, intelligent design and physiognomy.

Box 1.7: Characteristics of pseudoscience

• The use of vague, exaggerated, or untestable claims:  Many claims made by pseudoscience cannot be tested with evidence. As a result, they cannot be falsified, even if they are not true.
• An over-reliance on confirmation rather than refutation:  Any incident that appears to justify a pseudoscience claim is treated as proof of the claim. Claims are assumed true until proven otherwise, and the burden of disproof is placed on skeptics of the claim.
• A lack of openness to testing by other experts:  Practitioners of pseudoscience avoid subjecting their ideas to peer review. They may refuse to share their data and justify the need for secrecy with claims of proprietary or privacy.
• An absence of progress in advancing knowledge:  In pseudoscience, ideas are not subjected to repeated testing followed by rejection or refinement, as hypotheses are in true science. Ideas in pseudoscience may remain unchanged for hundreds — or even thousands — of years. In fact, the older an idea is, the more it tends to be trusted in pseudoscience.
• Personalization of issues:  Proponents of pseudoscience adopt beliefs that have little or no rational basis, so they may try to confirm their beliefs by treating critics as enemies. Instead of arguing to support their own beliefs, they attack the motives and character of their critics.
• The use of misleading language: Followers of pseudoscience may use scientific-sounding terms to make their ideas sound more convincing. For example, they may use the formal name dihydrogen monoxide to refer to plain old water (Miller, 2020).

Defenders of science have identified hallmark moves of the pseudoscientist, as shown in Box 1.7.

Scientifically literate graduates are able to improve society by combatting pseudoscience, as discussed in the section ‘Knowledge and appreciation of science in society’.

Australian researchers John Cook and Professor Stephan Lewandowsky wrote The Debunking Handbook , a guide to defending science as a more valid approach to explaining the world.

Click the drop down below to review the terms learned from this chapter.

Copyright note: Content from the following source is reproduced with permission from the copyright holder and is excluded from the Creative Commons Licence of this work. No further reproduction of this quotation is permitted without prior permission from the copyright holder.

• Gawande, A. (2016). The mistrust of science . The New Yorker. 

Additionally, content from:

• Miller, C. (2020). Human Biology . Thompson Rivers University is used under a CC-BY-NC 4.0 licence.
• Australian Government Science and Research Priorities is used under a CC-BY 2.0 licence. 

Alsan, M., & Wanamaker, M. (2016). Tuskegee and the health of black men. (Working Paper 22323.). National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w22323/w22323.pdf

Bauer, H.H. (1992). Scientific literacy and the myth of the scientific method. University of Illinois Press.

Belluz, J., Plumer, B., & Resnick, B. (2016, September 7). The 7 biggest problems facing science, according to 270 scientists. Vox. https://www.vox.com/2016/7/14/12016710/science-challeges-research-funding-peer-review-process

Chalmers, A.F. (2013). What is this thing called science? (4th ed.). Hackett Publishing.

Gauchat, G. (2012). Politicization of science in the public sphere: A study of public trust in the United States, 1974 to 2010. American Sociological Review, 77 (2), 167-187

Gawande, A. (2016, June 10). The mistrust of science. The New Yorker. https://www.newyorker.com/news/news-desk/the-mistrust-of-science

Gifford, F.A. (1964). The Martian canals according to a purely aeolian hypothesis. Icarus, 3 (2), 130-135. https://www.sciencedirect.com/science/article/abs/pii/0019103564900545

Haynes, R.D. (1994). From Faust to Strangelove: Representations of the scientist in western literature. John Hopkins University Press.

Hetherington, N.S. (1976). The British Astronomical Association and the controversy over canals on Mars. Journal of the British Astronomical Association, 86, 303-308. https://articles.adsabs.harvard.edu//full/1976JBAA…86..303H/0000303.000.html

Miller, C. (2020). Human Biology. Thompson Rivers University. https://humanbiology.pressbooks.tru.ca/

National Center for Science and Engineering Statistics. (2006). Chapter 7: Science and technology: Public attitudes and understanding. Retrieved July 22, 2016, from http://wayback.archive-it.org/5902/20150818094952/http://www.nsf.gov/statistics/seind06/c7/c7s2.htm

National Center for Science and Engineering Statistics. (2014). Chapter 7: Science and technology: Public attitudes and understanding. Retrieved July 22, 2016, from https://www.nsf.gov/statistics/seind14/content/chapter-7/chapter-7.pdf

Newton, I. (1999). Philosophiae Naturalis Principia Mathematica , (3rd ed.). (I. B. Cohen & A. Whitman 1999, Trans). University of California Press. https://archive.org/details/principiamathema0000newt/page/n9/mode/2up (Original work published in 1726).

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349 (6251), Aug 28.

Pitt, D., & Aubin, J.-M. (2012). Joseph Lister: Father of modern surgery. Canadian Journal of Surgery. 55 (5), E8-E9. https://doi.org/10.1503/cjs.007112

Tajika, A., Ogawa, Y., Takeshima, N., Hayasaka, Y., & Furukawa, T.A. (2015). Replication and contradiction of highly cited research papers in psychiatry: 10-year follow-up. British Journal of Psychiatry, 207 (4), 357-62. https://doi.org/10.1192/bjp.bp.113.143701

Yucel, R. (2013). Good practice guide (science): Threshold learning outcome 1 Understanding science. Australian Government Office for Learning and Teaching. https://www.acds.edu.au/wp-content/uploads/Science-Good-Practice-Guide-2013-TLO1.pdf

How To Do Science Copyright © 2022 by University of Southern Queensland is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

The scientific method and climate change: How scientists know

By Holly Shaftel, NASA's Jet Propulsion Laboratory

The scientific method is the gold standard for exploring our natural world. You might have learned about it in grade school, but here’s a quick reminder: It’s the process that scientists use to understand everything from animal behavior to the forces that shape our planet—including climate change.

“The way science works is that I go out and study something, and maybe I collect data or write equations, or I run a big computer program,” said Josh Willis, principal investigator of NASA’s Oceans Melting Greenland (OMG) mission and oceanographer at NASA’s Jet Propulsion Laboratory. “And I use it to learn something about how the world works.”

Using the scientific method, scientists have shown that humans are extremely likely the dominant cause of today’s climate change. The story goes back to the late 1800s, but in 1958, for example, Charles Keeling of the Mauna Loa Observatory in Waimea, Hawaii, started taking meticulous measurements of carbon dioxide (CO 2 ) in the atmosphere, showing the first significant evidence of rapidly rising CO 2 levels and producing the Keeling Curve climate scientists know today.

“The way science works is that I go out and study something, and maybe I collect data or write equations, or I run a big computer program, and I use it to learn something about how the world works.”- Josh Willis, NASA oceanographer and Oceans Melting Greenland principal investigator

Since then, thousands of peer-reviewed scientific papers have come to the same conclusion about climate change, telling us that human activities emit greenhouse gases into the atmosphere, raising Earth’s average temperature and bringing a range of consequences to our ecosystems.

“The weight of all of this information taken together points to the single consistent fact that humans and our activity are warming the planet,” Willis said.

The scientific method’s steps

The exact steps of the scientific method can vary by discipline, but since we have only one Earth (and no “test” Earth), climate scientists follow a few general guidelines to better understand carbon dioxide levels, sea level rise, global temperature and more.

• Form a hypothesis (a statement that an experiment can test)
• Make observations (conduct experiments and gather data)
• Analyze and interpret the data
• Draw conclusions
• Publish results that can be validated with further experiments (rinse and repeat)

As you can see, the scientific method is iterative (repetitive), meaning that climate scientists are constantly making new discoveries about the world based on the building blocks of scientific knowledge.

“The weight of all of this information taken together points to the single consistent fact that humans and our activity are warming the planet." - Josh Willis, NASA oceanographer and Oceans Melting Greenland principal investigator

The scientific method at work.

How does the scientific method work in the real world of climate science? Let’s take NASA’s Oceans Melting Greenland (OMG) campaign, a multi-year survey of Greenland’s ice melt that’s paving the way for improved sea level rise estimates, as an example.

• Form a hypothesis OMG hypothesizes that the oceans are playing a major role in Greenland ice loss.
• Make observations Over a five-year period, OMG will survey Greenland by air and ship to collect ocean temperature and salinity (saltiness) data and take ice thinning measurements to help climate scientists better understand how the ice and warming ocean interact with each other. OMG will also collect data on the sea floor’s shape and depth, which determines how much warm water can reach any given glacier.
• Analyze and interpret data As the OMG crew and scientists collect data around 27,000 miles (over 43,000 kilometers) of Greenland coastline over that five-year period, each year scientists will analyze the data to see how much the oceans warmed or cooled and how the ice changed in response.
• Draw conclusions In one OMG study , scientists discovered that many Greenland glaciers extend deeper (some around 1,000 feet, or about 300 meters) beneath the ocean’s surface than once thought, making them quite vulnerable to the warming ocean. They also discovered that Greenland’s west coast is generally more vulnerable than its east coast.
• Publish results Scientists like Willis write up the results, send in the paper for peer review (a process in which other experts in the field anonymously critique the submission), and then those peers determine whether the information is correct and valuable enough to be published in an academic journal, such as Nature or Earth and Planetary Science Letters . Then it becomes another contribution to the well-substantiated body of climate change knowledge, which evolves and grows stronger as scientists gather and confirm more evidence. Other scientists can take that information further by conducting their own studies to better understand sea level rise.

All in all, the scientific method is “a way of going from observations to answers,” NASA terrestrial ecosystem scientist Erika Podest, based at JPL, said. It adds clarity to our way of thinking and shows that scientific knowledge is always evolving.

Related Terms

• Climate Change
• Climate Science
• Earth Science

Explore More

2024 SARP West Closeout

On August 12-13, 24 students from the West Coast cohort of NASA’s Student Airborne Research Program (SARP) gathered at University of California, Irvine (UCI) to present their final research to a room of mentors, professors, family, and NASA personnel. SARP is an eight-week summer internship for undergraduate students, hosted in two cohorts: SARP West operates […]

NASA Analysis Shows Irreversible Sea Level Rise for Pacific Islands

Climate change is rapidly reshaping a region of the world that’s home to millions of people. In the next 30 years, Pacific Island nations such as Tuvalu, Kiribati, and Fiji will experience at least 8 inches (15 centimeters) of sea level rise, according to an analysis by NASA’s sea level change science team. This amount […]

Arctic Sea Ice Near Historic Low; Antarctic Ice Continues Decline

Arctic sea ice retreated to near-historic lows in the Northern Hemisphere this summer, likely melting to its minimum extent for the year on Sept.11, 2024, according to researchers at NASA and the National Snow and Ice Data Center (NSIDC). The decline continues the decades-long trend of shrinking and thinning ice cover in the Arctic Ocean. […]

Discover More Topics From NASA

Explore Earth Science

Earth Science in Action

Earth Science Data

Facts About Earth

• Skip to Page Content
• Skip to Navigation
• Skip to Search
• Skip to Footer
• COVID-19 Health and Safety
• Admissions and Ticketing
• Temporary Hall Closures
• Accessibility
• Field Trips
• Adult Group Visits
• Guided Tours
• Transportation
• Davis Family Butterfly Vivarium
• Invisible Worlds: Immersive Experience
• The Secret World of Elephants
• Turtle Odyssey
• Worlds Beyond Earth: Space Show
• Extinct and Endangered: Insects in Peril
• Grounded by Our Roots
• Ice Cold: An Exhibition of Hip-Hop Jewelry
• Opulent Oceans
• Portraits on Climate and Health: Dreams We Carry
• What's in a Name?
• Children & Family Programs
• Teen Programs
• Higher Education
• Adult Programs
• Educator Programs
• Evaluation, Research, & Policy
• Master of Arts in Teaching
• Online Courses for Educators
• Urban Advantage
• Climate Week NYC
• Viruses, Vaccines, and COVID-19
• The Science of COVID-19
• OLogy: The Science Website for Kids
• News & Blogs
• Science Topics
• Margaret Mead Festival
• Origami at the Museum
• Astrophysics
• Earth and Planetary Sciences
• Herpetology
• Ichthyology
• Ornithology
• Richard Gilder Graduate School
• Hayden Planetarium
• Center for Biodiversity and Conservation
• Institute for Comparative Genomics
• Southwestern Research Station
• Research Library
• Darwin Manuscripts Project
• Microscopy and Imaging Facility
• Science Conservation
• Computational Sciences
• Staff Directory
• Scientific Publications
• Ways to Donate
• Membership FAQ
• Benefit Events
• Corporate Support
• Planned Giving
• At the Museum
• Biodiversity
• Data Visualizations
• Dinosaurs and Fossils
• Earth and Climate
• In the Field
• Isaac Asimov Memorial Debate
• Kid Science
• Research and Collections
• SciCafe / Lectures

The Scientific Process

1. define a question to investigate.

As scientists conduct their research, they make observations and collect data. The observations and data often lead them to ask why something is the way it is. Scientists pursue answers to these questions in order to continue with their research. Once scientists have a good question to investigate, they begin to think of ways to answer it.

2. Make Predictions

Based on their research and observations, scientists will often come up with a hypothesis. A hypothesis is a possible answer to a question. It is based on: their own observations, existing theories, and information they gather from other sources. Scientists use their hypothesis to make a prediction, a testable statement that describes what they think the outcome of an investigation will be.

3. Gather Data

Evidence is needed to test the prediction. There are several strategies for collecting evidence, or data. Scientists can gather their data by observing the natural world, performing an experiment in a laboratory, or by running a model. Scientists decide what strategy to use, often combining strategies. Then they plan a procedure and gather their data. They make sure the procedure can be repeated, so that other scientists can evaluate their findings.

4. Analyze the Data

Scientists organize their data in tables, graphs, or diagrams. If possible, they include relevant data from other sources. They look for patterns that show connections between important variables in the hypothesis they are testing.

5. Draw Conclusions

Based on whether or not their prediction came true, scientists can then decide whether the evidence clearly supports or does not support the hypothesis. If the results are not clear, they must rethink their procedure. If the results are clear, scientists write up their findings and results to share with others. The conclusions they draw usually lead to new questions to pursue.