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• Design of Experiments

What Is Design of Experiments (DOE)?

Quality Glossary Definition: Design of experiments

Design of experiments (DOE) is defined as a branch of applied statistics that deals with planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters. DOE is a powerful data collection and analysis tool that can be used in a variety of experimental situations.

It allows for multiple input factors to be manipulated, determining their effect on a desired output (response). By manipulating multiple inputs at the same time, DOE can identify important interactions that may be missed when experimenting with one factor at a time. All possible combinations can be investigated (full factorial) or only a portion of the possible combinations (fractional factorial).

A strategically planned and executed experiment may provide a great deal of information about the effect on a response variable due to one or more factors. Many experiments involve holding certain factors constant and altering the levels of another variable. This "one factor at a time" (OFAT) approach to process knowledge is, however, inefficient when compared with changing factor levels simultaneously.

Many of the current statistical approaches  to designed experiments originate from the work of R. A. Fisher in the early part of the 20th century. Fisher demonstrated how taking the time to seriously consider the design and execution of an experiment before trying it helped avoid frequently encountered problems in analysis. Key concepts in creating a designed experiment include blocking, randomization, and replication.

• Blocking: When randomizing a factor is impossible or too costly, blocking lets you restrict randomization by carrying out all of the trials with one setting of the factor and then all the trials with the other setting.
• Randomization: Refers to the order in which the trials of an experiment are performed. A randomized sequence helps eliminate effects of unknown or uncontrolled variables.
• Replication: Repetition of a complete experimental treatment, including the setup.

A well-performed experiment may provide answers to questions such as:

• What are the key factors in a process?
• At what settings would the process deliver acceptable performance?
• What are the key, main, and interaction effects in the process?
• What settings would bring about less variation in the output?

A repetitive approach to gaining knowledge is encouraged, typically involving these consecutive steps:

• A screening design that narrows the field of variables under assessment.
• A "full factorial" design that studies the response of every combination of factors and factor levels, and an attempt to zone in on a region of values where the process is close to optimization.
• A response surface designed to model the response. 

When to Use DOE

Use DOE when more than one input factor is suspected of influencing an output. For example, it may be desirable to understand the effect of temperature and pressure on the strength of a glue bond.

DOE can also be used to confirm suspected input/output relationships and to develop a predictive equation suitable for performing what-if analysis.

Design of Experiments Template and Example

Setting up a DOE starts with process map. ASQ has created a design of experiments template  (Excel) available for free download and use. Begin your DOE with three steps:

• Acquire a full understanding of the inputs and outputs being investigated. A process flowchart  or process map can be helpful. Consult with subject matter experts as necessary.
• Determine the appropriate measure for the output. A variable measure is preferable. Attribute measures (pass/fail) should be avoided. Ensure the measurement system is stable and repeatable.

Design of Experiments: 3D Bar Chart

Design of Experiments: Pareto Chart

Conduct and Analyze Your Own DOE

Conduct and analyze up to three factors and their interactions by downloading the design of experiments template  (Excel).

Design of Experiments Summary

More complex studies can be performed with DOE. The above 2-factor example is used for illustrative purposes. A thorough discussion of DOE can be found in Juran’s Quality Handbook .

Contributed by Keith M. Bower.

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Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

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• Science Snippets Blog

What is DOE? Design of Experiments Basics for Beginners

[This blog was a favorite last year, so we thought you'd like to see it again. Send us your comments!]. Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.

This article is posted on our Science Snippets Blog .

Using  Design of Experiments (DOE)  techniques, you can determine the individual and interactive effects of various factors that can influence the output results of your measurements. You can also use DOE to gain knowledge and estimate the best operating conditions of a system, process or product.

DOE applies to many different investigation objectives, but can be especially important early on in a screening investigation to help you determine what the most important factors are. Then, it may help you optimize and better understand how the most important factors that you can regulate influence the responses or critical quality attributes.

Another important application area for DOE is in making production more effective by identifying factors that can reduce material and energy consumption or minimize costs and waiting time. It is also valuable for robustness testing to ensure quality before releasing a product or system to the market.

What’s the Alternative?

In order to understand why Design of Experiments is so valuable, it may be helpful to take a look at what DOE helps you achieve. A good way to illustrate this is by looking at an alternative approach, one that we call the  “COST”  approach. The COST ( C hange  O ne  S eparate factor at a  T ime) approach might be considered an intuitive or even logical way to approach your experimentation options (until, that is, you have been exposed to the ideas and thinking of DOE).

Let’s consider the example of a small chemical reaction where the goal is to find optimal conditions for yield. In this example, we can vary only two elements, or factors:

• the volume of the reaction container (between 500 and 700 ml), and
• the pH of the solution (between 2.5 and 5).

We change the experimental factors and measure the response outcome, which in this case, is the yield of the desired product. Using the COST approach, we can vary just one of the factors at time to see what affect it has on the yield.

So, for example, first we might fix the pH at 3, and change the volume of the reaction container from a low setting of 500ml to a high of 700ml. From that we can measure the yield.

Below is an example of a table that shows the yield that was obtained when changing the volume from 500 to 700 ml. In the scatterplot on the right, we have plotted the measured yield against the change in reaction volume, and it doesn’t take long to see that the best volume is located at 550 ml.

Next, we evaluate what will happen when we fix the volume at 550 ml (the optimal level) and start to change the second factor. In this second experimental series, the pH is changed from 2.5 to 5.0 and you can see the measured yields. These are listed in the table and plotted below. From this we can see that the optimal pH is around 4.5.

The optimal combination for the best yield would be a volume of 550 ml and pH 4.5. Sounds good right? But, let’s consider this a bit more.

Gaining a Better Perspective With DOE

What happens when we take more of a bird’s eye perspective, and look at the overall experimental map by number and order of experiments?

For example, in the first experimental series (indicated on the horizontal axis below), we moved the experimental settings from left to right, and we found out that 550 was the optimal volume.

Then in the second experimental series, we moved from bottom to top (as shown in the scatterplot below) and after a while we found out that the best yield was at experiment number 10 (4.5 pH).

The problem here is that we are not really certain whether the experimental point number 10 is truly the best one. The risk is that we have perceived that as being the optimum without it really being the case. Another thing we may question is the number of experiments we used. Have we used the optimal number of runs for experiments?

Zooming out and picturing what we have done on a map, we can see that we have only been exploiting a very small part of the entire experimental space. The true relationship between pH and volume is represented by the Contour Plot pictured below. We can see that the optimal value would be somewhere at the top in the larger red area.

So the problem with the COST approach is that we can get very different implications if we choose other starting points. We perceive that the optimum was found, but the other— and perhaps more problematic thing—is that we didn’t realize that continuing to do additional experiments would produce even higher yields.

How to Design Better Experiments

Instead, using the DOE approach, we can build a map in a much better way. First, consider the use of just two factors, which would mean that we have a limited range of experiments.  As the contour plot below shows, we would have at least four experiments (defining the corners of a rectangle.)

These four points can be optimally supplemented by a couple of points representing the variation in the interior part of the experimental design.

The important thing here is that when we start to evaluate the result, we will obtain very valuable information about the direction in which to move for improving the result. We will understand that we should reposition the experimental plan according to the dashed arrow.

However, DOE is NOT limited to looking at just two factors. It can be applied to three, four or many more factors.

If we take the approach of using three factors, the experimental protocol will start to define a cube rather than a rectangle. So the factorial points will be the corners of the cube.

In this way, DOE allows you to construct a carefully prepared set of representative experiments, in which all relevant factors are varied simultaneously.

DOE is about creating an entity of experiments that work together to map an interesting experimental region. So with DOE we can prepare a set of experiments that are optimally placed to bring back as much information as possible about how the factors are influencing the responses.

Plus, we will we have support for different types of regression models. For example, we can estimate what we call a linear model, or an interaction model, or a quadratic model. So the selected experimental plan will support a specific type of model.

Why Is DOE a Better Approach?

We can see three main reasons that DOE Is a better approach to experiment design than the COST approach.

DOE suggests the correct number of runs needed (often fewer than used by the COST approach)

DOE provides a model for the direction to follow

Many factors can be used (not just two)

In summary, the benefits of DOE are:

• An organized approach that connects experiments in a rational manner
• The influence of and interactions between all factors can be estimated
• More precise information is acquired in fewer experiments
• Results are evaluated in the light of variability
• Support for decision-marketing: map of the system (response contour plot)

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

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Muhammad Hassan

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Design of Experiments (DOE): A Comprehensive Overview on Its Meaning and Usage

The methodological framework known as Design of Experiments (DOE) is transformative in various disciplines, allowing researchers and practitioners the ability to plan, conduct, analyze, and interpret controlled tests to evaluate the factors that may influence a particular outcome. Historically grounded in agricultural and scientific research, DOE has burgeoned into an indispensable tool across a myriad of industries, including manufacturing, healthcare, and marketing. This article aims to elucidate the concepts and applications of Design of Experiments, providing a panoramic as well as an in-depth view of its various principles, types, statistical underpinnings, and practical applications.

Introduction to Design of Experiments (DOE)

Definition and basics of design of experiments.

Design of Experiments, at its core, is a structured methodological approach used to determine the relationship between factors affecting a process and the output of that process. It encompasses a vast array of strategies for testing hypotheses concerning the factors that could influence a particular variable of interest. The primary aim is not only to affirm the effect of these factors but also to quantify the extent to which they influence results, thus providing a scientific basis for decision-making.

Importance and relevance of DOE

In the contemporary landscape of problem-solving, the significance of DOE is multifaceted. It facilitates a systematic approach to experimentation that is both efficient and economical, reducing the number of trials needed to gather meaningful data. For instance, industries seeking to enhance their product quality can rely on DOE to guide them in identifying significant variables and their optimal settings. Moreover, DOE is an integral component of an effective problem solving techniques course or online certificate courses , as it equips students and professionals with the analytical skills necessary to tackle complex challenges.

Brief history and development of DOE

The foundations of Design of Experiments can be traced back to the early 20th century with the pioneering work of Ronald A. Fisher in the realm of agricultural research. His seminal contributions laid the groundwork for modern DOE, particularly in the context of controlling variation and maximizing information gain. As statistical methods have evolved, DOE has continued to grow in sophistication, integrating advances in computation and information technology to broaden its applicability across various fields.

Key Principles of Design of Experiments (DOE)

Randomization, what is randomization in doe.

Randomization is the backbone of a rigorous experimental design as it mitigates the effects of uncontrolled variables, or confounding factors, ensuring that the treatment groups are comparable. By assigning experimental units to treatment conditions randomly, researchers can confidently attribute differences in outcomes to the factors under study rather than to extraneous variables.

Benefits and significance of randomization

The paramount importance of randomization lies in its capacity to elevate the internal validity of an experiment. It reduces bias and ensures an unbiased estimate of the treatment effect, thus bolstering the credibility of the experimental results. This key principle is an assurance that the experiment's findings can be generalized beyond the study parameters.

Examples illustrating randomization

Consider a clinical trial for a new pharmaceutical product. If participants are randomized into the control and treatment groups, any difference observed in outcomes can be attributed to the drug's effectiveness rather than patient characteristics. This methodological rigor is what makes randomization indispensable in DOE.

Replication

Understanding replication in the context of doe.

Replication is the repeated application of certain conditions within an experiment to ensure that results are not anomalies. It adds precision to the experiment by allowing variance estimation, which is crucial for assessing the reliability of the findings.

Its role and importance in DOE

The role of replication in DOE cannot be overstressed. It affords the experimenter the ability to discern true effects from random errors, thus confirming the consistency of experimental results. When used judiciously, replication improves the power of the experiment, enabling more definitive conclusions.

Applicable examples of replication

For instance, verifying the strength of a newly engineered material could involve subjecting multiple samples to stress tests under identical conditions. The consistency in the failure threshold across these samples would indicate the reliability of the material's design as determined through replication.

Concept of blocking in DOE

Blocking is a technique used to account for variability among experimental units that cannot be eliminated. By grouping similar experimental units together and carrying out the same experimental conditions within these blocks, one can control the variables leading to variability within each group, thus reducing overall experimental error.

Advantages of using blocking in DOE

Employing blocking in an experiment minimizes the confounding effects of variables that are known but not directly of interest to the study. This approach increases the precision of the estimates of main effects and interactions by isolating the block-to-block variability from the treatment effects. It is particularly useful when one wants to account for heterogeneity among subjects or any other nuisance variable.

Real-world blocking examples

In an agricultural study, fields could be blocked by soil type before the application of different fertilization regimes. This ensures that variation due to soil composition does not skew the results, thus giving a clearer understanding of the fertilizers' effectiveness.

Categories of Design of Experiments (DOE)

Full-factorial design, overview and basic understanding.

Full-factorial design involves the investigation of every possible combination of factors and their levels. Each treatment combination is applied to separate units in an experiment, making it a comprehensive approach that allows for the assessment of both main effects and interactions between factors.

Strengths and limitations

While the full-factorial design provides a wealth of information about the factors in question, it can be resource-intensive and impractical when dealing with a large number of factors. However, if resources allow, this type of design delivers the most exhaustive data on the effects and possible interactions.

Use case example of full-factorial design

A manufacturer could use a full-factorial design to understand the impact of temperature and pressure on the strength of a welded joint. By varying these factors systematically across all levels, the manufacturer could determine the optimal conditions for the welding process that ensure joint strength.

Fractional-factorial Design

Definition and understanding of the concept.

Fractional-factorial design is an efficient variation of the full-factorial design that investigates only a subset of the possible combinations of factor levels. By strategically selecting a fraction of the full design, researchers can still gather information on the most crucial factors influencing an outcome while significantly reducing the number of experiments.

Advantages and drawbacks

This design's main advantage is its economy in terms of time and resources. Nonetheless, this comes with the potential drawback of confounding, which is when two or more factor effects cannot be separated due to the design's reduced size. Researchers must balance the need for information with the constraints of their experimental budget.

Case study demonstrating the use of fractional-factorial design

A tech company wanting to assess the design features impacting user engagement might employ a fractional-factorial design to investigate a manageable subset of combinations rather than exhaust every potential variation, thereby identifying critical design elements efficiently.

Response Surface Design

Explaining response surface design.

Response Surface Design (RSD) is focused on modeling the relationship between a response and a set of quantitative variables. It is particularly useful when the goal is optimization, as RSD can identify the levels of factors that lead to the best possible outcome.

Pros and cons of using Response Surface Design

RSD exhibits potency in exploring complex, nonlinear relationships and can be pivotal in honing in on optimal conditions. However, constructing a suitable response surface model often requires a more significant number of experimental runs compared to simpler designs, and interpreting the resulting model may necessitate a substantial level of statistical expertise.

Applicable example of response surface design

An example might involve a food scientist employing RSD to optimize a recipe for flavor and texture by manipulating ingredient ratios and cooking times. By analyzing the response surface, the scientist can pinpoint the exact conditions that yield the best culinary result.

Statistical Analysis in Design of Experiments (DOE)

Role of statistical analysis in doe.

Statistical analysis is fundamental in DOE, as it transforms experimental data into meaningful insights. Here, analytic techniques are employed to discern patterns, test hypotheses, and derive conclusions that can withstand scrutiny within the scientific community.

Various statistical techniques applied in DOE

The array of statistical methods applied within DOE is expansive, encompassing t-tests, ANOVA, regression analysis, and multivariate techniques, among others. Selecting the appropriate method hinges on the complexity of the data structure and the goals of the research.

Examples demonstrating the importance of statistical analysis in DOE

For instance, a multinational corporation might conduct a series of experiments to improve a product feature. Utilizing ANOVA, researchers can determine whether differences in quality metrics across various production sites are statistically significant, guiding quality improvement efforts.

Practical Applications of Design of Experiments (DOE)

Doe in product and process development.

DOE plays a critical role in new product development and process refinement. By assessing the impact of variable changes, designers and engineers can develop superior products and streamline processes for improved performance and cost-efficiency.

The role of DOE in industrial manufacturing

In the context of industrial manufacturing, DOE is an invaluable asset. It assists in identifying key process factors and optimizing manufacturing conditions, resulting in enhanced quality control and reduced production costs—a boon for competitiveness in the market.

Case studies showing the impact of DOE in various fields

Case studies from the pharmaceutical industry to aerospace engineering demonstrate DOE's vital contributions. Whether optimizing drug formulations or adjusting flight parameters, DOE's applications are incredibly diverse and contribute significantly to scientific and technological advancements.

Conclusion: Future Trends and Developments in Design of Experiments (DOE)

Current trends in doe.

The recent trends in DOE point towards the integration of more advanced computational techniques, like machine learning algorithms, to handle complex, high-dimensional data sets and to refine predictive models for experimental outcomes.

Future outlook and potential advancements in DOE

Looking ahead, we may witness the further development of real-time analytics in DOE, enabling even more dynamic exploration of experiment spaces, perhaps leading to adaptive experimentation that could revolutionize fields as disparate as genomics and supply chain management.

Final thoughts and recommendations for further studies in DOE

As the landscape of DOE progresses, so does the need for educational pursuits such as online certificate courses in this area, fostering a new generation of adept experimenters equipped with equipped with the latest tools and techniques. Investigators are encouraged to undertake further studies in DOE to harness its full potential—a testament to the enduring relevance and flexibility of this methodological powerhouse.

What are the key characteristics of a well-designed experiment in research methodology?

Well-designed experiment essentials, clarity in purpose.

A well-crafted experiment begins with a crystal-clear objective. Researchers should articulate their primary questions. These drive the experiment. Specific goals guide the study's structure. Precise objectives leave no room for ambiguity. Clear aims ensure focused data collection. This results in robust and relevant findings. Clarity underscores every experiment layer.

Rigorous Planning

Rigorous planning underpins scientific integrity. Researchers craft detailed protocols. These serve as experiments' blueprints. They outline every step and contingency. Careful design minimizes unwanted variables' intrusion. It ensures the experiment can test hypotheses effectively. Predefined procedures guarantee the study's repeatability. Other scientists can replicate the study with ease.

Controlled Conditions

Experiments thrive under control. Researchers strive for controlled environments. They manage variables meticulously. Control is not absolute but optimized. Key is distinguishing between independent and dependent variables. Independent variables undergo deliberate changes. Researchers measure dependent variables for effect assessments. Control groups provide a comparison benchmark. They remain untouched, isolating the independent variable’s impact.

Randomization and Blinding

Randomization promotes objectivity. It mitigates selection bias. Participants or samples receive random allocation. This ensures equal distribution of confounding variables. Blind or double-blind setups conceal information. Subjects or researchers remain unaware of certain details. This prevents bias from influencing outcomes. Blinding strengthens an experiment's credibility.

Sufficient Sample Size

Sample size holds crucial importance. It must be statistically sufficient. Adequacy enables valid generalizations. Small samples undermine the study's validity. They invite chance-driven anomalies. Optimal size depends on the expected effect size. Power analysis often determines the required sample. Researchers seek a balance. Overly large samples waste resources. Insufficient samples yield inconclusive results.

Ethical Considerations

Ethical concerns stand paramount. Researchers uphold rigorous ethical standards. Participants give informed consent. They understand the experiment's nature. Ethical treatment extends beyond humans. Animal studies require humane conduct. Ethical oversight comes from institutional review boards. They assess risks versus benefits. They ensure research integrity.

Data Analysis Plan

Planning extends to data analysis. Researchers must decide on this before data collection. They determine which statistical tests fit their data. They predefine significance levels. This minimizes data dredging after the fact. A thorough plan prevents misleading analytic practices. It directs researchers to honest interpretations.

Transparent Reporting

Finally, clarity in reporting is essential. Researchers describe their methods in detail. They disclose all conditions, variables, and results. Transparency fosters trust in the findings. It enables other scientists to verify results. Clear reporting is the capstone of a well-designed experiment.

In summary, these characteristics thread through exemplary research. They elevate experiments from mere inquiry to scientific evidence. They bolster confidence in the knowledge we gain. Researchers must adhere to these tenets. Only then can we rely on their discoveries.

How does Design of Experiments contribute towards the efficacy and efficiency of a study?

Unveiling the power of design of experiments, efficient resource utilization.

Design of Experiments (DoE) stands pivotal. It optimizes resource allocation. Fewer resources yield comprehensive data. This translates into significant cost savings. Each experiment harnesses maximal information gain.

Enhanced Understanding

DoE allows for a better grasp of variables. It clarifies the interaction between factors. Such insights foster informed decision-making. They also streamline the research process considerably.

Systematic Approach

The approach of DoE is inevitably systematic. It eliminates the hit-and-miss experiments. Every trial becomes a well-thought-out step. Researchers work with clear objectives and methods.

Reduction of Experimental Runs

One key benefit is reduced experimental runs. DoE leverages factorial designs. These designs assess multiple factors simultaneously. They aid in understanding complex interactions swiftly. Thus, they reduce the number of required experiments.

Data Quality Improvement

With DoE, data quality improves. The method ensures a structured data collection. Bias minimization is a direct outcome. Consistent and high-quality data is the result. This robustness adds credibility to the study.

Accelerated Timeframes

DoE can significantly hasten experimentation. A thorough initial planning phase foresees potential obstacles. It also identifies the most critical factors early on. Time saved here quickens overall study completion.

Risk Mitigation

Risk reduction is another aspect. DoE helps in anticipating variability. Researchers understand possible outcomes better. Preemptive measures are then easier to implement.

Decision-Making Precision

DoE offers precise guidance for decision-making. It sorts critical from trivial factors. Decisions are therefore more data-driven. Their precision enhances the study's value.

Optimization of Conditions

It aids in the optimization of experimental conditions. Optimal settings are quickly identified. This leads to better product quality or process efficiency.

Design of Experiments revolutionizes research efficacy and efficiency. Researchers see DoE as more than a tool. It's a strategic ally in scientific inquiry. Its methodical, efficient, and data-centric approach is unmatched. The results? Enhanced understanding, quality, and breakthroughs in less time.

What are the potential pitfalls or challenges that researchers may encounter when using Design of Experiments and how can these be mitigated?

Understanding design of experiments.

Design of Experiments ( DoE ) serves as a powerful tool. It enables researchers to systematically explore complexities in various fields. In DoE, potential confounding factors can skew results. Researchers must handle these with care. Proper experimental design is thus pivotal.

Addressing Complex Interactions

Interactions among variables often complicate DoE. These interactions can mask true effects. Identify key factors before experimentation. Focus on a manageable number of interactions. Simplify the complexity of your study.

Adequate Sample Size and Replicability

Sample size directly affects the power of an experiment. Too small a size may miss vital nuances. Ensure the sample size supports your study's objective. Replicability is a cornerstone of scientific research. Repetition validates the initial findings. Plan duplicate runs to confirm results.

Controlling External Variability

Uncontrolled external factors introduce noise. This noise can reduce the clarity of findings. Maintain strict environmental control where possible. Strive for consistency across all experimental conditions.

Choosing the Right Design

Selecting an inappropriate design can lead to misleading conclusions. Understand the strengths and weaknesses of different designs. Tailor the design to your specific research question.

Handling Missing or Outlying Data

Data may go missing or fall outside expected ranges. Develop a plan for dealing with such data. Consider statistical methods like imputation for missing values. Apply outlier tests to determine the fate of anomalies.

Statistical Proficiency

DoE demands statistical knowledge. Interpret results with statistical confidence. Derive meaningful insights without overstepping the data's bounds. Stay educated on statistical methods relevant to DoE.

Ethical concerns must guide any experimental work. Ensure that your design minimizes potential harm. Adhere to ethical standards throughout your research.

Mitigating Challenges in DoE

Researchers can take steps to improve the robustness of their experimental designs.

- Plan Thoroughly : Pre-experimental planning is critical. Anticipate challenges and lay out a clear roadmap.

- Pilot Studies : Conduct preliminary tests. These can highlight unforeseen issues. Address these before proceeding with the full-scale experiment.

- Educate Yourself : Nurture a deep understanding of DoE principles. Attend workshops and seminars. Stay abreast of advancements in experimental design.

- Consult Experts : Do not hesitate to seek advice. Collaborate with statisticians and experts. Their insights can help craft a more sound experiment.

- Software Tools : Leverage software designed for DoE. These can automate complex statistical computations. Ensure accuracy in your design and analysis.

- Documentation : Keep detailed records of every step. Document the rationale behind decisions. This transparency aids in replicating and validating results.

Engagement with these strategies can lead to stronger, more reliable experiments. It enables researchers to maneuver through potential pitfalls confidently. Acknowledge the complexities of DoE. Strive for rigor in design and execution. The reward is valid, reproducible knowledge that advances your field.

He is a content producer who specializes in blog content. He has a master's degree in business administration and he lives in the Netherlands.

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Design of experiments

What is design of experiments.

Design of experiments (DOE) is a systematic, efficient method that enables scientists and engineers to study the relationship between multiple input variables (aka factors) and key output variables (aka responses). It is a structured approach for collecting data and making discoveries.

When to use DOE?

• To determine whether a factor, or a collection of factors, has an effect on the response.
• To determine whether factors interact in their effect on the response.
• To model the behavior of the response as a function of the factors.
• To optimize the response.

Ronald Fisher first introduced four enduring principles of DOE in 1926: the factorial principle, randomization, replication and blocking. Generating and analyzing these designs relied primarily on hand calculation in the past; until recently practitioners started using computer-generated designs for a more effective and efficient DOE.

Why use DOE?

DOE is useful:

• In driving knowledge of cause and effect between factors.
• To experiment with all factors at the same time.
• To run trials that span the potential experimental region for our factors.
• In enabling us to understand the combined effect of the factors.

To illustrate the importance of DOE, let’s look at what will happen if DOE does NOT exist.

Experiments are likely to be carried out via trial and error or one-factor-at-a-time (OFAT) method.

Trial-and-error method

Test different settings of two factors and see what the resulting yield is.

Say we want to determine the optimal temperature and time settings that will maximize yield through experiments.

How the experiment looks like using trial-and-error method:

1. Conduct a trial at starting values for the two variables and record the yield:

2. Adjust one or both values based on our results:

3. Repeat Step 2 until we think we've found the best set of values:

As you can tell, the  cons of trial-and-error  are:

• Inefficient, unstructured and ad hoc (worst if carried out without subject matter knowledge).
• Unlikely to find the optimum set of conditions across two or more factors.

One factor at a time (OFAT) method

Change the value of the one factor, then measure the response, repeat the process with another factor.

In the same experiment of searching optimal temperature and time to maximize yield, this is how the experiment looks using an OFAT method:

1. Start with temperature: Find the temperature resulting in the highest yield, between 50 and 120 degrees.

    1a. Run a total of eight trials. Each trial increases temperature by 10 degrees (i.e., 50, 60, 70 ... all the way to 120 degrees).

    1b. With time fixed at 20 hours as a controlled variable.

    1c. Measure yield for each batch.

2. Run the second experiment by varying time, to find the optimal value of time (between 4 and 24 hours).

    2a. Run a total of six trials. Each trial increases temperature by 4 hours (i.e., 4, 8, 12… up to 24 hours).

    2b. With temperature fixed at 90 degrees as a controlled variable.

    2c. Measure yield for each batch.

3. After a total of 14 trials, we’ve identified the max yield (86.7%) happens when:

• Temperature is at 90 degrees; Time is at 12 hours.

As you can already tell, OFAT is a more structured approach compared to trial and error.

But there’s one major problem with OFAT : What if the optimal temperature and time settings look more like this?

We would have missed out acquiring the optimal temperature and time settings based on our previous OFAT experiments.

Therefore,  OFAT’s con  is:

• We’re unlikely to find the optimum set of conditions across two or more factors.

How our trial and error and OFAT experiments look:

Notice that none of them has trials conducted at a low temperature and time AND near optimum conditions.

What went wrong in the experiments?

• We didn't simultaneously change the settings of both factors.
• We didn't conduct trials throughout the potential experimental region.

The result was a lack of understanding on the combined effect of the two variables on the response. The two factors did interact in their effect on the response!

A more effective and efficient approach to experimentation is to use statistically designed experiments (DOE).

Apply Full Factorial DOE on the same example

1. Experiment with two factors, each factor with two values. 

These four trials form the corners of the design space:

2. Run all possible combinations of factor levels, in random order to average out effects of lurking variables .

3. (Optional) Replicate entire design by running each treatment twice to find out experimental error :

4. Analyzing the results enable us to build a statistical model that estimates the individual effects (Temperature & Time), and also their interaction.

It enables us to visualize and explore the interaction between the factors. An illustration of what their interaction looks like at temperature = 120; time = 4:

You can visualize, explore your model and find the most desirable settings for your factors using the JMP Prediction Profiler .

Summary: DOE vs. OFAT/Trial-and-Error

• DOE requires fewer trials.
• DOE is more effective in finding the best settings to maximize yield.
• DOE enables us to derive a statistical model to predict results as a function of the two factors and their combined effect.

It's the last day for these savings

Design of Experiments: Definition, How It Works, & Examples

In the world of research, development, and innovation, making informed decisions based on reliable data is crucial. This is where the Design of Experiments (DoE) methodology steps in. DoE provides a structured framework for designing experiments that efficiently identify the factors influencing a process, product, or system.

DoE provides a strong tool to help you accomplish your objectives, whether you work in software development, manufacturing, pharmaceuticals, or any other industry that needs optimization.

This article by SkillTrans will analyze for you a better understanding of DoE through many different contents, including:

What is Design Of Experiments

Design Of Experiments Examples

Design Of Experiments Software

What Is Doe In Problem Solving

And What Is Doe In Testing

First of all, let's learn the definition of DoE.

What is Design of Experiments?

According to Wikipedia , DoE is defined as follows: 

“The design of experiments (DOE or DOX), also known as experiment design or experimental design, is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation.”

To put it more simply, Design of Experiments (DoE) is a powerful statistical methodology that revolutionizes the way we conduct experiments and gain insights. At its core, DoE is a systematic and efficient approach to experimentation, allowing researchers, engineers, and scientists to study the relationship between multiple input variables (factors) and key output variables (responses).

Why DoE is Superior to Traditional Testing

Traditional testing methods often rely on a "one-factor-at-a-time" (OFAT) approach, where only one factor is changed while holding others constant. 

This method has several limitations:

Time-Consuming: Testing each factor individually can be incredibly slow, especially when dealing with numerous variables.

Misses Interactions: OFAT fails to capture how factors might interact with each other, leading to incomplete or even misleading results.

Inefficient: It often requires a large number of experiments to gain a comprehensive understanding of a system.

How DoE Works

DoE takes a different approach by carefully planning experiments where multiple factors are varied simultaneously according to a predetermined design. This allows for the investigation of both the individual effects of each factor (main effects) and the combined effects of multiple factors (interaction effects) . 

By doing so, DoE provides a more holistic and accurate picture of the system being studied.

Statistical Power of DoE

DoE uses statistical analysis to interpret experiment outcomes. DoE can quantify the impact of the major factors influencing the response, identify the best settings or conditions, and identify the components that influence the response by using a variety of statistical models.

Benefits of DoE

Reduced Costs: DoE often requires fewer experimental runs than OFAT, saving time and resources.

Improved Understanding: DoE provides a deeper understanding of complex systems by uncovering interactions between factors.

Robust Solutions: DoE helps identify solutions that are more robust to variations in factors, leading to greater reliability.

Faster Optimization: By simultaneously exploring a wider range of conditions, DoE can accelerate the optimization process.

Applications for DoE can be found in many different areas, such as software development, marketing, manufacturing, medicines, and agriculture. It is a priceless tool for innovation and advancement in a variety of sectors due to its capacity to quickly and effectively address complicated difficulties.

We will learn more about the areas where DoE is commonly used in the next section.

Design of Experiments Examples

DoE has a proven track record of solving complex problems and driving innovation across a wide range of sectors. Here are some examples:

Design of Experiments Examples in Manufacturing

DoE is used to optimize manufacturing processes like casting, molding, machining, and assembly . It helps identify optimal settings for temperature, pressure, cycle time, and other variables, leading to improved quality, reduced scrap, and lower costs.

Design of Experiments Examples in Pharmaceuticals

DoE plays a crucial role in drug development, helping to determine optimal dosages, identify the most effective combinations of ingredients, and optimize manufacturing processes for quality and consistency.

Design of Experiments Examples in Agriculture

DoE is widely used in agriculture to optimize crop yields, improve soil fertility, and develop more sustainable farming practices. It helps researchers understand the complex interactions between environmental factors, plant genetics, and farming techniques.

Design of Experiments Examples in Software Development

DoE is applied in software testing to optimize test coverage, prioritize test cases, and identify software vulnerabilities. It also helps developers understand how different code changes impact performance and reliability.

Design of Experiments Examples in Marketing

DoE is utilized in marketing to optimize pricing strategies, advertising campaigns, and product launches. It helps marketers understand how different factors influence consumer behavior, allowing them to tailor their strategies for maximum impact.

These examples are just a glimpse into the vast potential of DoE. To better understand DoE's contribution to different fields, let's take a look at DoE in more detail.

Design of Experiments Software

While the principles of DoE are rooted in statistics and experimental design, the emergence of sophisticated software tools has democratized the methodology, making it accessible to a wider audience. These tools simplify the entire DoE workflow , from initial planning to final analysis, empowering users to design, execute, and interpret experiments with confidence.

Key Features and Benefits of DoE Software

Experiment design.

DoE software helps users choose the best experimental design depending on their objectives, considerations, and available resources. It facilitates the creation of effective experimental plans, randomization of runs, and design matrices.

Statistical Modeling

The statistical models that explain the connection between variables and responses are automatically created by the software. Response surface models, analysis of variance ( ANOVA ), and linear regression are among the models it can fit.

Data Analysis

DoE software offers strong analytical capabilities for data analysis , such as effect estimation, model diagnostics, and hypothesis testing. It assists users in locating important variables, estimating their influence, and choosing the best configurations.

Optimization

Optimization algorithms are a common feature of DoE software packages, which assist users in determining the combination of factor values that maximizes or minimizes a desired result.

Visualization

To assist users in efficiently interpreting and communicating their findings, DoE software provides a variety of visualization tools, including Pareto charts , interaction plots, and response surface plots.

Popular DoE Software Options

Here are a few well-known DoE software you might want to look into:

JMP is a feature-rich statistical software package with strong DoE capabilities that was developed by SAS. It provides a large selection of designs, sophisticated statistical modeling capabilities, and an intuitive user interface.

A well-liked statistics program with plenty of DoE tools and an intuitive user interface is Minitab . It provides a wide range of designs, simple analysis tools, and lucid visualizations.

Design-Expert

Specialized DoE software called Design-Expert concentrates on response surface methodology (RSM). It offers an easy-to-use interface for creating, evaluating, and refining complicated interaction experiments.

Stat-Ease 360

Stat-Ease 360 , a more comprehensive version of Design-Expert, interfaces with Python to enable custom scripting and sophisticated analysis.

Other Options

There are numerous other DoE software options available, each with its own strengths and target audience. Some examples include Cornerstone, MODDE, and Unscrambler .

The intricacy of the trials, financial limitations, features that are wanted, and the user's degree of statistical competence all influence the choice of DoE software. In order to provide consumers the opportunity to test out the features and functioning before deciding to buy, many software companies offer free trials.

DoE in Problem Solving

Identifying effective solutions and determining the underlying causes of complex problems can be challenging due to the presence of various interacting components. Design of Experiments (DoE) provides a methodical, data-driven approach to resolving these issues and making wise choices. 

Here's a closer look at the DoE problem-solving process :

Define the Problem with Metrics

The first step in using metrics and Design of Experiments to effectively address a problem is to precisely define the pertinent, quantifiable problem. For example, state the challenge as "reduce defect rate by 20% within six months" rather than aiming for something as abstract as "improve product quality." 

For the purpose of problem-solving, clearly define your aims and objectives and what you want to accomplish through experimenting. 

Furthermore, ascertain which important parties will be impacted by the issue and its resolution, and make sure that their requirements and viewpoints are taken into account at every stage of the process.

Identify Factors with Potential Impact

Start by thinking and making a list of every potential input variable that can have an impact on the result or response variable in order to uncover elements that could have an impact. These variables may include uncontrollable ones like raw material variability or ambient circumstances, as well as controllable ones like temperature, pressure, or ingredient proportions. 

After you have a complete list, rank the elements according to how they might affect the answer. You can determine the relative relevance of each item by utilizing previous information, professional judgment, or preliminary evidence. 

Furthermore, take into account how different elements interact with one another, as some may have an effect that is different from each of them alone.

Design the Experiment with Statistical Rigor

The first step in creating an experiment with statistical rigor is choosing an acceptable experimental design that takes into account the number of variables, the desired level of detail, and the resources that are available. Response surface designs, factorial designs, and fractional factorial designs are examples of common designs. 

Subsequently, ascertain the necessary number of experimental runs to attain statistically significant outcomes, taking into account variables like the intended confidence level, response variability, and the target effect size. 

In order to reduce the influence of uncontrollable circumstances and maximize the reliability and objectivity of the results, finally arrange the experimental runs in a random sequence.

Analyze the Results with Statistical Tools

In order to use statistical tools to analyze the outcomes, first gather data from the experiments and analyze it with applicable procedures like regression analysis, analysis of variance (ANOVA), or other pertinent statistical approaches. 

Determine which statistically significant variables actually affect the response. Calculate the ideal settings for each significant element by quantifying its effect size. 

To ensure a thorough grasp of how various variables affect the result, evaluate the interactions between components and ascertain their impact on the response.

Implement Solutions with Data-Driven Confidence

Start by creating workable solutions based on the findings of your study in order to execute solutions with confidence that is informed by evidence. These fixes could include updating designs, introducing new tactics, altering formulas, and adjusting process settings. 

To make sure the solutions are effective, validate them with more trials or pilot studies. After the solutions are put into place, keep an eye on them and evaluate their effects over time. Use the information gathered to make any necessary additional improvements or modifications.

DoE in Testing

The field of testing has seen a revolution in the evaluation and optimization of products and processes thanks to the Design of Experiments (DoE) approach. It offers a methodical and effective way to look into the various ways that variable inputs affect a system's quality, dependability, and performance across a broad spectrum of circumstances.

Why DoE is Essential for Testing

Traditional testing methods often involve changing one factor at a time, which can be time-consuming and may miss critical interactions between factors. DoE, on the other hand, allows testers to simultaneously manipulate multiple factors according to a carefully designed plan. 

This enables them to:

Identify Optimal Settings

DoE helps determine the combination of factor settings that yield the best possible results, whether it's maximizing a desired output (e.g., yield, efficiency) or minimizing an undesirable one (e.g., defects, variability).

Reduce Variability

DoE can assist in identifying methods to lessen or regulate system performance variability by understanding the various elements that contribute to this variability and how to achieve more consistent and predictable results.

Enhance Robustness

DoE can identify solutions that are robust to variations in factors, ensuring that the product or process performs well even under different operating conditions or with varying inputs.

Accelerate Testing

DoE can save time and money by strategically choosing experimental runs and evaluating the collected data, which can lower the number of experiments needed to produce trustworthy results.

Gain Deeper Insights

DoE provides a deeper knowledge of the behavior of the system by revealing intricate interconnections between components, going beyond just identifying key ones.

Examples of DoE in Testing

Here are a few examples of DoE in testing that you might find useful:

Software Testing

DoE is used to optimize software performance , identify bugs and vulnerabilities, and ensure compatibility across different platforms and configurations. For example, a software company might use DoE to test the impact of different hardware configurations, network conditions, and user behaviors on the performance of their application.

Product Testing

DoE is employed to evaluate the performance and reliability of products under various conditions, such as temperature, humidity, vibration, and stress. This helps manufacturers identify design weaknesses, improve product robustness, and ensure compliance with quality standards. For instance, an electronics company might use DoE to test the durability of their smartphones under extreme temperatures and humidity levels.

Process Testing

DoE is applied to optimize manufacturing processes, improve yield, reduce defects, and enhance overall efficiency. For example, a chemical company might use DoE to optimize the reaction conditions for a chemical synthesis process, such as temperature, pressure, and reactant concentrations.

Medical Device Testing

DoE is used to assess the effectiveness and safety of medical devices across a variety of patient groups, usage scenarios, and environmental settings. This ensures that medical gadgets function consistently well in real-world circumstances and satisfy regulatory standards.

A flexible approach, Design of Experiments enables organizations to solve complicated challenges, obtain deeper insights, and make data-driven decisions. You can reach a new level of productivity and creativity in your industry by adopting DoE and making use of the appropriate software solutions.

In search of DoE Courses? From introductory to advanced courses in Design of Experiments , SkillTrans has a lot to offer. Look through our collection to select the ideal training to advance your knowledge!

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Maximizing Efficiency and Accuracy with Design of Experiments

Updated: April 21, 2024 by Ken Feldman

Design of experiments (DOE) can be defined as a set of statistical tools that deal with the planning, executing, analyzing, and interpretation of controlled tests to determine which factors will impact and drive the outcomes of your process. 

This article will explore two of the common approaches to DOE as well as the benefits of using DOE and offer some best practices for a successful experiment. 

Overview: What is DOE? 

Two of the most common approaches to DOE are a full factorial DOE and a fractional factorial DOE . Let’s start with a discussion of what a full factorial DOE is all about.

The purpose of the full factorial DOE is to determine at what settings of your process inputs will you optimize the values of your process outcomes. As an example, if your output is the fill level of a bottle of carbonated drink, and your primary process variables are machine speed, fill speed, and carbonation level, then what combination of those factors will give you the desired consistent fill level of the bottle?

With three variables, machine speed, fill speed, and carbonation level, how many different unique combinations would you have to test to explore all the possibilities? Which combination of machine speed, fill speed, and carbonation level will give you the most consistent fill? The experimentation using all possible factor combinations is called a full factorial design. These combinations are called Runs .  

We can calculate the total number of runs using the formula # Runs=2^k, where k is the number of variables and 2 is the number of levels, such as (High/Low) or (100 ml per minute/200 ml per minute). 

But, what if you aren’t able to run the entire set of combinations of a full factorial? What if you have monetary or time constraints, or too many variables? This is when you might choose to run a fractional factorial , also referred to as a screening DOE , which uses only a fraction of the total runs. That fraction can be one-half, one-quarter, one-eighth, and so forth depending on the number of factors or variables. 

While there is a formula to calculate the number of runs, suffice it to say you can just calculate your full factorial runs and divide by the fraction that you and your Black Belt or Master Black Belt determine is best for your experiment.

3 benefits of DOE 

Doing a designed experiment as opposed to using a trial-and-error approach has a number of benefits.  

1. Identify the main effects of your factors

A main effect is the impact of a specific variable on your output. In other words, how much does machine speed alone impact your output? Or fill speed?

2. Identifying interactions

Interactions occur if the impact of one factor on your response is dependent upon the setting of another factor. For example if you ran at a fill speed of 100 ml per minute, what machine speed should you run at to optimize your fill level? Likewise, what machine speed should you run at if your fill speed was 200 ml per minute? 

A full factorial design provides information about all the possible interactions. Fractional factorial designs will provide limited interaction information because you did not test all the possible combinations. 

3. You can determine optimal settings for your variables 

After analyzing all of your main effects and interactions, you will be able to determine what your settings should be for your factors or variables. 

Why is DOE important to understand? 

When discussing the proper settings for your process variables, people often rely on what they have always done, on what Old Joe taught them years ago, or even where they feel the best setting should be. DOE provides a more scientific approach. 

Distinguish between significant and insignificant factors

Your process variables have different impacts on your output. Some are statistically important, and some are just noise. You need to understand which is which.

The existence of interactions

Unfortunately, most process outcomes are a function of interactions rather than pure main effects. You will need to understand the implications of that when operating your processes. 

Statistical significance 

DOE statistical outputs will indicate whether your main effects and interactions are statistically significant or not. You will need to understand that so you focus on those variables that have real impact on your process.

An industry example of DOE 

A unique application of DOE in marketing is called conjoint analysis. A web-based company wanted to design its website to increase traffic and online sales. Doing a traditional DOE was not practical, so leadership decided to use conjoint analysis to help them design the optimal web page.

The marketing and IT team members identified the following variables that seemed to impact their users’ online experience: 

• loading speed of the site
• font of the text
• color scheme
• primary graphic motion
• primary graphic size 
• menu orientation

They enlisted the company’s Master Black Belt to help them do the experiment using a two-level approach.

In a conjoint analysis DOE, you would create mockups of the various combinations of variables. A sample of customers were selected and shown the different mockups. After viewing them, the customer then ranked the different mockups from most preferred to least preferred. The ranking provided the numerical value of that combination. To keep matters simple, they went with a quarter fraction design, or 16 different mockups. Otherwise, you’re asking customers to try and differentiate their preference and rank way too many options.

Once they gathered all the data and analyzed it, they concluded that menu orientation and loading speed were the most significant factors. This allowed them to do what they wanted with font, primary graphic, and color scheme since they were not significant.

3 best practices when thinking about DOE 

Experiments take planning and proper execution, otherwise the results may be meaningless. Here are a few hints for making sure you properly run your DOE. 

1. Carefully identify your variables

Use existing data and data analysis to try and identify the most logical factors for your experiment. Regression analysis is often a good source of selecting potentially significant factors. 

2. Prevent contamination of your experiment

During your experiment, you will have your experimental factors as well as other environmental factors around you that you aren’t interested in testing. You will need to control those to reduce the noise and contamination that might occur (which would reduce the value of your DOE).

3. Use screening experiments to reduce cost and time

Unless you’ve done some prior screening of your potential factors, you might want to start your DOE with a screening or fractional factorial design. This will provide information as to potentially significant factors without consuming your whole budget. Once you’ve identified the best potential factors, you can do a full factorial with the reduced number of factors.

Frequently Asked Questions (FAQ) about DOE

What does “main effects” refer to.

The main effects of a DOE are the individual factors that have a statistically significant effect on your output. In the common two-level DOE, an effect is measured by subtracting the response value for running at the high level from the response value for running at the low level. The difference is the effect of that factor.

How many runs do I need for a full factorial DOE?

The formula for calculating the number of runs of a full factorial DOE is # Runs=X^K where X is the number of levels or settings, and K is the number of variables for factors.

Are interactions in DOE important? 

Yes. Sometimes your DOE factors do not behave the same way when you look at them together as opposed to looking at the factor impact individually. In the world of pharmaceuticals, you hear a lot about drug interactions. You can safely take an antihistamine for your allergies. You can also safely take an antibiotic for your infection. But taking them both at the same time can cause an interaction effect that can be deadly.

In summary, DOE is the way to go

A design of experiments (DOE) is a set of statistical tools for planning, executing, analyzing, and interpreting experimental tests to determine the impact of your process factors on the outcomes of your process. 

The technique allows you to simultaneously control and manipulate multiple input factors to determine their effect on a desired output or response. By simultaneously testing multiple inputs, your DOE can identify significant interactions you might miss if you were only testing one factor at a time. 

You can either use full factorial designs with all possible factor combinations, or fractional factorial designs using smaller subsets of the combinations.

About the Author

Ken Feldman

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What is Design of Experiments (DOE)?

• Design of Experiments is a framework that allows us to investigate the impact of multiple different factors on an experimental process
• It identifies and explores the interactions between factors and allows researchers to optimize the performance and robustness of processes or assays
• The old conventional approach to scientific experimentation (one-factor-at-a-time, or “OFAT”) are limited in both the number of variables which you can investigate and, critically, preclude investigating how variables interact
• This blog introduces the principles of Design of Experiments, beginning with its origins
• If you’d like to keep learning about DOE after you're done with this article, make sure to  check out our other DOE blogs , download our  DOE for biologists ebook , or watch our  DOE Masterclass webinar series .

DOE Masterclass (Part 1)

What doe is and how it transforms your biological research.

A richer understanding of biological complexity .

What makes a good cup of tea?

A discussion about whether adding milk before or after the tea influences the taste may seem a long way from ensuring that Escherichia coli  expresses a particular plasmid, optimizing vaccine formulation and delivery, 1,2 or dissecting the intricacies of metabolomics. 3  

But it's closer than you think.

After all, scientific revolutions can arise from everyday observations: a falling apple inspired Isaac Newton to formulate gravitational theory.

Of all the places for a revolution to start, a tea party in 1920s Cambridge laid the foundations of a statistical technique called Design of Experiments (DOE), which allows researchers to investigate the impact of simultaneously changing multiple factors.

Design of Experiments (DOE): a surprising origin story .

One afternoon some dons, their wives, and guests were having afternoon tea. One lady said she could taste whether tea or milk was poured into the cup first. (Some people believe that hot tea scorches milk, for example.)

The statistician Ronald Fisher, who attended the tea party, devised an experiment to test her claim. The lady was randomly given four cups in which tea was poured before the milk and four where the milk was poured first.

To analyze the interactions between the factors (milk and tea), Ronald devised Fisher’s Exact Test. This determines if any association between the two categorical variables is statistically significant. 4  

As Figure 1 shows, even four cups of tea can give rise to numerous possible permutations. But this only scratches the surface of tea–making’s complexity.

A perfect cup of tea depends on multiple other factors, such as the blend, brewing time, and the addition of sugar. In other words, making a perfect cup of tea is complex and multidimensional. DOE allows researchers to investigate the effect of changing multiple factors simultaneously.

0= Incorrect; X=correct

Figure 1: Distribution factors assuming that the lady could not distinguish that milk was added before tea (null hypothesis)

In a series of blogs, we’re going to explore the basis of DOE, who should consider DOE, and some ways in which this methodology helps experimental biologists deal with life’s inherent complexity. We’ll begin, however, by going back to school.

School's out... and so is OFAT (one-factor-at-a-time) experimentation .

Our school teachers advocated a one-factor-at-a-time (OFAT) approach to scientific experimentation. So, pick a variable (factor) and vary the value (levels), while keeping everything else constant. 

That may be fine in the school lab. Unfortunately, biology doesn’t work that way .

Biological variation, for example, can mean results vary randomly around a set point even in a constant environment. Sample collection, transport, preservation, and measurement systems can introduce further sources of variation. 5  

DOE helps us understand emergent phenomena .

Biological phenomena, even life itself, are typically emergent . In other words, new patterns and structures appear through the interactions between autonomous elements. 6

Every living thing consists of numerous autonomous parts that interact dynamically and unpredictably as part of one or more systems. This means, for example, that you can’t predict cellular diversity by examining nucleotides’ chemical and physical properties.

You also can’t predict the products of cognition by analyzing neuroarchitecture. Emergence is one reason biologists often lack well-developed, robust theoretical frameworks to guide their experiments.

DOE is better for exploring biological complexity .

Most biological processes are complicated, complex, and multidimensional. 7 So, changing one factor probably changes something else .

For example, it isn't possible to fully understand the functional consequences of changing a protein's structure without understanding all the contexts in which it appears. Its interactions within biological networks are what really define its function, so even minor changes can produce a plethora of unpredictable down- and upstream effects.

DOE allows the explorations of complex, multidimensional experimental design spaces despite such methodological, biological, or chemical variations. 7  

OFAT ignores biology’s inherent complexity . It is limited in both the number of variables that you can investigate and, critically, it precludes any investigation of how variables interact.

It’s a bit like trying to analyze the perfect cup of tea by ignoring the temperature of the water, brew time, and blend, and instead just focusing on whether you add the milk first or second. 

Figure 2: OFAT may convince you you’ve found an optimum… but it may not be the real one.

Unsurprisingly, OFAT can often identify the wrong system state as the optimum .

Moreover, the lack of well-developed, robust theoretical frameworks can result in unconscious cognitive bias: it’s all too easy to develop OFAT experiments that confirm, rather than test, hypotheses. 7

DOE helps avoid unconscious cognitive bias and allows researchers to look behind the curtain of biological complexity to see what’s really going on.

What is Design of Experiments (DOE) ?

Design of Experiments is a framework that allows us to investigate the impact of multiple different factors—changed simultaneously—on an experimental process .

DOE also identifies and explores the interactions between those factors. This allows us to optimize the performance and robustness of our processes or assays.

Let’s apply DOE to another simple example: the strawberries you may have with the tea you’ve just added your milk to... 

DOE looks at different ranges within factors .

Numerous quantitative factors (e.g. hours of sunlight, grams of plant food, and liters of water) or qualitative factors (e.g. the cultivar) can influence the strawberry crop ( Figure 2 ).

You need to begin by setting a realistic range for each factor. So, testing 1kg of plant food could prove toxic and expensive. Strawberries also need plenty of water to ensure juiciness; applying 1ml of water would be difficult to accurately achieve and, possibly, trigger drought stress responses.

Figure 3: Design of Experiments (DOE) through the example of strawberries. How different factors and levels may impact the yield, weight, and taste of a crop of strawberries

DOE tests many factors at the same time .

The responses we are looking for in this experiment are the yield, the weight, and the taste of the strawberries. You may decide you want a high yield of the tastiest strawberries. 

Design of experiments allows you to test numerous factors to determine which make the largest contributions to yield and taste.

Based on this, you can fine-tune the experiment and use DOE to determine which combination of factors at specific levels gives the optimal balance of yield and taste.

You can also compare different levels for given factors, such as whether a cultivar from nursery A produces a higher yield, better taste, or both than a plant from nursery B.

DOE lets you investigate specific outcomes .

Design of Experiments also allows you to investigate specific outcomes (what combinations produce the best balance of yield and taste in a robust way) and reduce variability (define new conditions so the strawberry yield remains the same).

Cost may be another consideration. DOE lets you balance trade-offs , such as what conditions produce the most cost-effective way to achieve the highest yield of strawberries.

DOE Masterclass: Design of Experiments 101 for biologists .

DOE helps reduce the time, materials, and experiments needed to yield a given amount of information compared with OFAT.

As well as these savings, DOE achieves higher precision and reduced variability when estimating the effects of each factor or interaction than using OFAT. It also systematically estimates the interaction between factors, which is not possible with OFAT experiments.

This article offers only a very brief introduction to DOE.

Dive deeper into Design of Experiments:

• Why should I use Design of Experiments in Life Sciences

When and how to use Design of Experiments (DOE)

• DOE in the real world: when and how to use Design of Experiments
• Types of DOE design: a users' guide
• The DOE process: an overview
• Overcoming barriers to Design of Experiments (DOE)
• 3 reasons why DOE rollouts fail and what to do about it
• Four ways to cut R&D costs with DOE

Well, I’m off for a cup of tea.

Interested in learning more about DOE? Subscribe to our free DOE training course , where you can learn DOE in 6 minutes a day, or catch the full series DOE Masterclass webinar series on our YouTube page .

• Ahl PL, Mensch C, Hu B et al. Accelerating vaccine formulation development using design of experiment stability studies. Journal of Pharmaceutical Sciences 2016;105:3046-3056
• Hashiba A, Toyooka M, Sato Y et al. The use of design of experiments with multiple responses to determine optimal formulations for in vivo hepatic mRNA delivery. Journal of Controlled Release 2020;327:467-476
• Surowiec I, Johansson E, Torell F et al. Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics. Metabolomics 2017;13:114
• Bi J and Kuesten C. Revisiting Fisher’s ‘Lady Tasting Tea’ from a perspective of sensory discrimination testing. Food Quality and Preference 2015;43:47-52
• Badrick T. Biological variation: Understanding why it is so important? Practical Laboratory Medicine 2021;23:e00199
• Ikegami T, Mototake Y-i, Kobori S et al. Life as an emergent phenomenon: studies from a large-scale boid simulation and web data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2017;375:20160351
• Lendrem DW, Lendrem BC, Woods D et al. Lost in space: design of experiments and scientific exploration in a Hogarth Universe. Drug Discovery Today 2015;20:1365-1371

Michael "Sid" Sadowski, PhD

Michael Sadowski, aka Sid, is the Director of Scientific Software at Synthace, where he leads the company’s DOE product development. In his 10 years at the company he has consulted on dozens of DOE campaigns, many of which included aspects of QbD.

Other posts you might be interested in

Why Design of Experiments (DOE) is important for biologists

Why quality by design (qbd) is vital for pharmaceutical r&d.

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Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Design of Experiments

Last Updated October 19, 2023

Optimal performance of your process is locked inside current performance, just waiting to be discovered. The optimal process can emerge once all the variables are adjusted appropriately. The Design of Experiments (DOE) tool helps align process variables and arrange them to ensure optimal performance.

What is Design of Experiments?

Design of Experiments (DOE) is a Six Sigma tool that helps project teams determine the effects that the inputs of a process have on the final product. DOE helps uncover the critical relationships between variables in a process that are often hidden under all of the data and identifies the most critical inputs that must be modified to ensure optimal process performance. Once Design of Experiments identifies the critical inputs of the process, it helps project teams understand the impact that modifying the variables will have on process performance.

Design of Experiments Terminology

Six Sigma Design of Experiments is a systematic process that breaks down the variables of production and analyzes each one. This process has its own set of terms that we must understand to become conversant with how the technique works.

• Factor – This is an independent variable, or a variable you have control over. In DOE, factors are deliberately modified to determine the point of optimal performance.
• Level – This is a measurement of how much a factor has been modified. Levels can be discrete or numeric.
• Run – An experiment typically done at two or three levels for every factor; each separate level constitutes an experimental run.
• Response – The outcome of the run.
• Replication – Refers to multiple sets of experimental runs. Replication provides even more data and greater confidence in evaluating the results.

How to Apply Design of Experiments

Design of Experiments terminology is more clearly understood when applied to a practical example. Suppose a project team used DOE to optimize the process for baking a cake.

• Factors  – The factors in the process, the variables that the team controls, consist of the ingredients of sugar, flower, eggs, water and oil. The oven is also a factor. These are inputs into the process.
• Levels  – The levels in the cake baking process are the temperature of the oven, the cooking time and the amount of each ingredient used. These are the potential settings of each factor.
• Response  – The cake is the output or the response of the run. The characteristics of the output, cake, are then evaluated  to determine if the levels in this particular run lead to optimal performance, or a cake that ranks well in taste, color, and consistency.

Design of Experiments provides many ways to bake a better cake:

• Comparing alternatives – The team can test the results of using two different types of the same ingredient by keeping other factors the same.
• Reducing variability – Determining how the levels can be changed so that the cake is always the same quality.
• Targeting an output – Deciding what changes to make in the ingredients and amounts of ingredients used that make the cake just right.
• Evaluating tradeoffs – Discovering how to produce the best response (cake) possible by using the simplest factors (the smallest amount of ingredients).

Selecting the Factors

Many inputs determine the output of a process. The factors that are most relevant to the end result are the ones most important to DOE. These factors can be identified by the project team in a brainstorming session. In ordinary circumstances, where time and budget are finite, the team should limit the experiment to six or seven key factors. These factors are controlled by setting them at different levels for each run.

Setting the Levels

Once the factors have been identified, the team must determine the settings at which these factors will be run for the experiment. The example of baking a cake demonstrates that some factors are measured in numbers, such as oven temperature and cooking time. Some factors are also qualitative, such as how much icing to use. These are measured in categories and are converted into coded units for linear regression analysis.

The more levels that are identified for each factor, the more trials will be required to test these levels. To ensure that an optimal number of levels are selected, focus on a range of interest. This range includes settings used in the normal course of operations and may also include settings of more extreme scenarios. The greater the difference in factor levels, the easier it becomes to measure variance.

Evaluating the Response

The response is the outcome of the experiment. Outcomes are helpful in improving the process when they can be measured in quantitative terms, rather than in qualitative attributes. A response that is quantifiable makes the experiment well suited to the additional scrutiny of statistical regression techniques.

Design of experiments allows inputs to be changed to determine how they affect responses. Instead of testing one factor at a time while holding others constant, DOE reveals how interconnected factors respond over a wide range of values, without requiring the testing of all possible values directly. This helps reveal secrets hidden behind the different factors and levels in a process and allows the project team to understand the process much more rapidly.

Once completed, Design of Experiments helps the Six Sigma project team better identify the combination of inputs that lead to the highest-quality product or service.

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• Design of Experiments

What is Design of Experiments (DOE)? Your Method to Optimize Results

Learn about Design of Experiments and how it can help you achieve optimal results from your experiments

What is Design of Experiments (DOE)?

Design of Experiments (DOE) is a systematic method used in applied statistics to evaluate the many possible alternatives in one or more design variables. It allows the manipulation of various input variables (factors) to determine what effect they could have in order to get the desired output (responses) or improve on the result.

In DoE, experiments are being used to find an unknown outcome or effect, to test a theory, or to demonstrate an already known effect. T hey are done by scientists and engineers, among others, in order to understand which inputs have a major impact on output and what input levels should be targeted to reach a desired outcome (output). Simply put, DoE is a way to collect information during the experiment and then determine what factors or which processes could lead to the desired result.

History of Design of Experiments

The term “Design of Experiments,” also known as experimental design, was coined by Ronald Fisher in the 1920s. He used it to describe a method of planning experiments to find the best combination of factors that affect the response or output. It is used to reduce design expenses because analysis of input parameters or factors gives way in identifying waste and which processes can be eliminated. It also helps remove complexities and streamlining the design process for cost management in the manufacturing process.

The key concept behind this methodology is that there is a relationship between the factors affecting the response. ISixSigma defined it as determining the “cause and effect relationships” of factors. Therefore, a complete experimental plan consists of the combination of factors used to evaluate their effects on the response.

Components of Experimental Design

MoreSteam gave a simple illustration to explain the components of the experiment—the three aspects that need to be analyzed in the design experiments—and understanding the meaning of each is crucial in defining DoE.

Cake-baking Process showing the Components of Experimental Design source: MoreSteam

• Controllable variables – pertains to factors that can be modified or changed in an experiment or a process. For example, in the cake-baking process, these factors may include what will be used in baking such as the oven, sugar, flour, and eggs.
• Uncontrollable variables – pertains to factors that cannot be changed. For example, in the cake baking process, this may be the room temperature in the kitchen. They must be recognized to understand how they may affect the response.
• Levels or settings of each factor – they pertain to the quantity or quality that will be used in the experiment. In the cake-baking process example, this includes the oven temperature setting and the quantity of sugar, flour, and eggs.
• Responses – pertains to the outcome of the process that gages the desired effect. In the cake-baking example, the taste, appearance, and consistency of the cake are the responses. They are influenced by the factors and their levels. This is the purpose of experimentation—analyzing each factor to determine which of them provides the best overall outcome or the same quality.

Purpose of Design of Experiments

Experimental design is not only conducted by scientists or engineers. It can be used by different industries who want to maximize the results they’re getting. DoE is conducted to:

Compare alternatives

Conducting experimental design allows you to look at different alternatives. It helps in making an informed decision on what to use or what to change. This methodology can also be used to discover the best combination of alternatives in the experiment.

Maximize process response

With DoE, the factors and their levels are checked and see which of them when used are giving the exact quality in the response.

Reduce variations

Excess variations in the process are the cause of added expense. It affects the cycle time that causes quality differences. With DoE, factors are identified, responses are interpreted, and waste is eliminated or changed.

Process Improvement

Performing a DOE can uncover significant issues that are typically missed when conducting an experiment. These areas will be corrected thus improving the process.

Evaluate the effect of change/s

With DoE, you can determine the effects of changes made with the factors and their levels that influences the response.

Quality Control

DOE can also help improve manufacturing efficiency by identifying factors that reduce material and energy use, costs, and waiting time. It is also used to test a product or system before releasing it to market.

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Examples and Applications of Experimental Design

Below are some practical applications or examples on where DoE is applied:

Pharmaceutical Industry

In the pharmaceutical industry, DOE is most typically used throughout the drug formulation and manufacturing phases. Qualitty is critical for drug products because health and safety of consumers are at risk when a product doesn’t meet the standards. DoE is used in drug testing, reducing impurities in the process of making drugs, before releasing it for consumer use.

DoE is used especially in drugs that are best delivered via a time-release schedule,. It means that it takes time to dissolve slowly in the body. Because one component of DoE is the settings of factors, performing an experimental runs are applicable here.

Fast-Moving Consumer Goods (FMCG) industry

FMCG industry is a part of consumer goods industry that includes all the products which are sold to the general public by any means such as retail stores, internet or by phone. These are mostly used by the consumers in their daily life and may include food, drinks, health and hygiene, cosmetics, household appliances, among others. DoE helps in comparing alternatives or options to get the response where price will be cheaper but does not compromise on quality.

Product Design

DoE is a useful tool for determining specific factors affecting defect levels in a product, which may be used to improve the design of the product.

6 Steps Design of Experiments

Standard DoE processes are often structured around seven or fewer steps. The steps in experimental design will take you through the process of determining what is the best response that you could use in your study, workplace, or procedures.

Steps of Design of Experiments (DOE). Source: JMP

• Describe – this is a critical part wherein you determine what is your goal or what do you want to achieve, which is followed by determining what is your desired response. The first step includes determining your goal, your desired response, and factors.
• Specify – this is the part where you need to specify what variables describe the physical situation, or the factors.
• Design – this is the part where you generate an experimental design model from which you will draw evaluations after run/s or trial/s.
• Collect– this is the part where you execute the design, collect information from the run/s and record the responses that you get.
• Fit – this is the part where you review the responses if it does fit in the generated experimental design model. In some cases, runs should be repeated in order to correct model ambiguity.
• Predict – the last step wherein you predict the results and determine which factor best optimizes the response.

SafetyCulture (formerly iAuditor) for Experimental Design

Why safetyculture.

Perform a DoE to optimize any procedure in your workplace and integrate your experimentation with SafetyCulture . A powerful tool used by multiple industries in performing a more convenient and efficient way to monitor, collect, record, inspect, and audit data.

With the support of SafetyCulture as a Design of Experiments software , engineers, scientists, manufacturers, and researchers, among others, can do the following during the experimental design:

• Monitor and identify if there are process drifts and changes in variables during the run with sensors and the monitoring feature.
• Record the responses that you generate through your experimental runs in a secured cloud and easily access it anywhere when needed through the app.
• Specify which factors have defects using Quality Control Check Sheet.
• Modify quality inspections templates tailored to your specifications to support your experimentation.
• Notify or alert your team about modifications on data collected real-time.

Browse checklists helpful to experimental design:

• DMAIC Template Checklist
• Manufacturing Quality Control Checklist
• Product Evaluation Template Checklist
• DMADV Template Checklist

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Design of Experiments

DOE is also is also known as Designed Experiments or Experimental Design and begins by identifying the major factors that could cause process variance. The Designed Experiments tool contains three elements. For example, if the DOE were used on the process of making a pizza the elements would include the following:

• Factors – These are inputs to the process. Factors are considered as either controllable or uncontrollable variables. Factors in the pizza example include the oven, dough, sauce, and toppings.
• Levels – These are the potential settings of each factor. The levels in the pizza making process are the temperature of the oven, the cooking time and the amount of sauce and toppings used.
• Response – This is the output of the experiment. DOE strives for a measurable output that is influenced by the factors and their differing levels.  The response or output from the example is how the pizza tastes.

Select the Factors

There can be a number of inputs in a process that can affect the output. The factors that are most relevant to the end result are the ones most important to DOE. These factors can be selected by the project team in a brainstorming session. In ordinary circumstances where time and budget are finite, the team should limit the experiment to six or seven key factors. These factors are controlled by setting them at different levels for each run.

Set the Levels

Once the factors have been selected, the team must determine the settings at which these factors will be run for the experiment. The example of cooking a pizza demonstrates that some factors are measured in numbers, such as oven temperature and cooking time. Some factors are qualitative such as which toppings are used; they are measured in categories and are converted into coded units for linear regression analysis .

The more levels that are identified for each factor the more trials will be required to test these levels. To ensure that an optimal number of levels are selected, focus on a range of interest. This range includes settings used in the normal course of operations and also may include settings of more extreme scenarios. The greater the difference in factor levels the easier it becomes to measure variance.

Evaluate the Response

The response is the outcome of the experiment. Outcomes are most helpful in improving the process when they can be measured in quantitative terms rather than in qualitative attributes. A response that is quantifiable makes the experiment well suited to the additional scrutiny of statistical regression techniques.

Design of Experiments allows inputs to be changed to determine how they affect responses. Instead of testing one factor at a time while holding others constant, DOE reveals how interconnected factors respond over a wide range of values, without requiring the testing of all possible values directly. This helps the project team understand the process much more rapidly.

What is Design Of Experiments (DOE)?-An Essential Guide

The true method of knowledge is an experiment William Blake.

Design of Experiments DOE statistics are used by human resources, marketers, continuous improvement leaders, sales managers, engineers & many others. When applied to a product or process the output can be a lower variation of outputs, increased quality performance, higher yields, faster development time, lower costs, and increased consumer satisfaction. So, In this article, we will be discussing the statistical design.

Selecting the Factors

Setting the levels, assessing the response, the complete process of a design of experiments doe: , which items to avoid when conducting a design of experiments doe, key takeaways.

• Factors – These are inputs to the process. Factors are either controllable or uncontrollable variables.
• Levels – They are the possible settings of each factor. So, These consist of the number of raw materials that involves in each batch.
• Response – This is the output of the experiment. DOE strives for a measurable output that is influenced by the factors and their differing levels.

Check this also: 10 Innovative Tech Accessories For Smartphone Users

There can be various inputs in a process that can affect the output. Design of Experiments DOE or statistical design software concentrates on the factors that have the highest impact on the final product. When the project team is familiar with a process, its members can find the factors, or raw materials, in a process with a simple brainstorming session during digital transformation . In simple circumstances when budget & time is less, the team should restrict the experiment to 6 or 7 key factors. So, These factors are controlled by setting them at different levels for each run.

When the factors are identified, this mix of raw materials can be modified in each run to determine the impact on the final product. A project team focuses on a range of interests while business transformation . Furthermore, The range of interest involves mixes of raw materials or levels that are most likely to occur in the normal course of production.

The range can also comprise levels of raw materials for more extreme scenarios. So, The greater the difference in factor levels the easier it becomes to measure variance. When we select the factors, the team should determine the settings at which these factors will run for the experiment.

Leader’s Tip:

Identify objectives and factors: clearly define what you want to learn and the variables affecting the outcome..

The outcome of the experiment is the Response. Outcomes are most useful in improving the process when they can measure in quantitative terms instead of in qualitative ones. A quantifiable response makes the experiment well suited to the additional scrutiny of statistical regression techniques.

Design of Experiments DOE permits inputs to alter to find out how they affect responses. Instead of testing one factor at a time & holding other constant, DOE Six S igma discloses how interconnected factors respond over a broad range of values, without needing the testing of all possible values directly. Design of Experiments helps Six Sigma project teams identify the mix of raw materials that develop the highest quality product.

• Define objective.
• Collect knowledge about the process.
• Develop a list & choose your variables.
• Assign levels to variables.
• Conduct experiments.
• Data analysis & conclusions.
• Unwarranted assumptions of the process.
• Undesirable combinations of the factors.
• Too large or small design sizes.
• Imprecise measurement.
• Unacceptable prediction error.
• Undesirable run order.

Suggested for you –   TQM (Total Quality Management) in VUCA World

Design of Experiments DOE is a structured method for executing experiments. It’s beneficial in product development, process development, and process modification as well. Relying on the problem, the benefits of the DOE strategy include quicker time to market, down development costs, lower operating costs, and decreased cost of poor quality.

Randomize and replicate: Random assignment reduces bias, while replication ensures reliable conclusions.

What is the DOE approach to design of experiments?

Design of experiments (DOE) is  a systematic, efficient method that enables scientists and engineers to study the relationship between multiple input variables (aka factors) and key output variables (aka responses) . It is a structured approach for collecting data and making discoveries.

Is DOE a statistical tool?

Design of Experiments (DOE) is  a statistical tool  available to engineers that can be used to evaluate single changes or multiple changes to a process at once and predict the resulting change to the output of the process.

What is DOE statistical analysis?

Design of experiments (DOE) is defined as a branch of applied statistics that deals with planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters.

DOE optimizes resource utilization and accelerates learning by systematically varying experimental conditions. Control groups help isolate treatment effects, enhancing the validity and reliability of experimental results. Understanding interactions between factors allows for fine-tuning processes and improving overall performance.

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What is a designed experiment?

A designed experiment is a series of runs, or tests, in which you purposefully make changes to input variables at the same time and observe the responses. In industry, designed experiments can be used to systematically investigate the process or product variables that affect product quality. After you identify the process conditions and product components that affect product quality, you can direct improvement efforts to enhance a product's manufacturability, reliability, quality, and field performance.

For example, you work at an offset printing company where some customers have complained of pages coming unattached from their books' bindings. You suspect several factors: glue temperature, paper type, and cooling time. You want to determine which factors, or combinations of factors, significantly affect the effectiveness of your company's binding technique. When you create a designed experiment, Minitab automatically randomizes the run order of the design and displays the design in your worksheet. The run order is the ordered sequence of factor combinations. You can use the worksheet to record your responses when you do your experiment.

• Screening designs
• Factorial designs
• Response surface designs
• Mixture designs
• Taguchi designs

Designed experiments are often done in four phases: planning, screening (also called process characterization), optimization, and verification.

Our intent is to provide only a brief introduction to the design of experiments. There are many resources that provide a thorough treatment of these methods.

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Keyboard Shortcuts

1.1 - a quick history of the design of experiments (doe).

The textbook we are using brings an engineering perspective to the design of experiments. We will bring in other contexts and examples from other fields of study including agriculture (where much of the early research was done) education and nutrition. Surprisingly the service industry has begun using design of experiments as well.

  All experiments are designed experiments, it is just that some are poorly designed and some are well-designed.  

Engineering Experiments Section  

If we had infinite time and resource budgets there probably wouldn't be a big fuss made over designing experiments. In production and quality control we want to control the error and learn as much as we can about the process or the underlying theory with the resources at hand. From an engineering perspective we're trying to use experimentation for the following purposes:

• reduce time to design/develop new products & processes
• improve performance of existing processes
• improve reliability and performance of products
• achieve product & process robustness
• perform evaluation of materials, design alternatives, setting component & system tolerances, etc.

We always want to fine-tune or improve the process. In today's global world this drive for competitiveness affects all of us both as consumers and producers.

Robustness is a concept that enters into statistics at several points. At the analysis, stage robustness refers to a technique that isn't overly influenced by bad data. Even if there is an outlier or bad data you still want to get the right answer. Regardless of who or what is involved in the process - it is still going to work. We will come back to this notion of robustness later in the course (Lesson 12).

Every experiment design has inputs. Back to the cake baking example: we have our ingredients such as flour, sugar, milk, eggs, etc. Regardless of the quality of these ingredients we still want our cake to come out successfully. In every experiment there are inputs and in addition, there are factors (such as time of baking, temperature, geometry of the cake pan, etc.), some of which you can control and others that you can't control. The experimenter must think about factors that affect the outcome. We also talk about the output and the yield or the response to your experiment. For the cake, the output might be measured as texture, flavor, height, size, or flavor.

Four Eras in the History of DOE Section  

Here's a quick timeline:

• R. A. Fisher & his co-workers
• Profound impact on agricultural science
• Factorial designs, ANOVA
• Box & Wilson, response surfaces
• Applications in the chemical & process industries
• Quality improvement initiatives in many companies
• CQI and TQM were important ideas and became management goals
• Taguchi and robust parameter design, process robustness
• The modern era, beginning circa 1990, when economic competitiveness and globalization are driving all sectors of the economy to be more competitive.

Immediately following World War II the first industrial era marked another resurgence in the use of DOE. It was at this time that Box and Wilson (1951) wrote the key paper in response surface designs thinking of the output as a response function and trying to find the optimum conditions for this function. George Box died early in 2013. And, an interesting fact here - he married Fisher's daughter! He worked in the chemical industry in England in his early career and then came to America and worked at the University of Wisconsin for most of his career.

The Second Industrial Era - or the Quality Revolution

W. Edwards Deming

The importance of statistical quality control was taken to Japan in the 1950s by W Edward Deming. This started what Montgomery calls a second Industrial Era, and sometimes the quality revolution. After the second world war, Japanese products were of terrible quality. They were cheaply made and not very good. In the 1960s their quality started improving. The Japanese car industry adopted statistical quality control procedures and conducted experiments which started this new era. Total Quality Management (TQM), Continuous Quality Improvement (CQI) are management techniques that have come out of this statistical quality revolution - statistical quality control and design of experiments.

Taguchi, a Japanese engineer, discovered and published a lot of the techniques that were later brought to the West, using an independent development of what he referred to as orthogonal arrays. In the West, these were referred to as fractional factorial designs. These are both very similar and we will discuss both of these in this course. He came up with the concept of robust parameter design and process robustness.

The Modern Era

Around 1990 Six Sigma, a new way of representing CQI, became popular. Now it is a company and they employ a technique which has been adopted by many of the large manufacturing companies. This is a technique that uses statistics to make decisions based on quality and feedback loops. It incorporates a lot of previous statistical and management techniques.

Clinical Trials

Montgomery omits in this brief history a major part of design of experimentation that evolved - clinical trials. This evolved in the 1960s when medical advances were previously based on anecdotal data; a doctor would examine six patients and from this wrote a paper and published it. The incredible biases resulting from these kinds of anecdotal studies became known. The outcome was a move toward making the randomized double-blind clinical trial the gold standard for approval of any new product, medical device, or procedure. The scientific application of the statistical procedures became very important.