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Hypothesis Testing – A Complete Guide with Examples

Published by Alvin Nicolas at August 14th, 2021 , Revised On October 26, 2023

In statistics, hypothesis testing is a critical tool. It allows us to make informed decisions about populations based on sample data. Whether you are a researcher trying to prove a scientific point, a marketer analysing A/B test results, or a manufacturer ensuring quality control, hypothesis testing plays a pivotal role. This guide aims to introduce you to the concept and walk you through real-world examples.

What is a Hypothesis and a Hypothesis Testing?

A hypothesis is considered a belief or assumption that has to be accepted, rejected, proved or disproved. In contrast, a research hypothesis is a research question for a researcher that has to be proven correct or incorrect through investigation.

What is Hypothesis Testing?

Hypothesis testing  is a scientific method used for making a decision and drawing conclusions by using a statistical approach. It is used to suggest new ideas by testing theories to know whether or not the sample data supports research. A research hypothesis is a predictive statement that has to be tested using scientific methods that join an independent variable to a dependent variable.  

Example: The academic performance of student A is better than student B

Characteristics of the Hypothesis to be Tested

A hypothesis should be:

• Clear and precise
• Capable of being tested
• Able to relate to a variable
• Stated in simple terms
• Consistent with known facts
• Limited in scope and specific
• Tested in a limited timeframe
• Explain the facts in detail

What is a Null Hypothesis and Alternative Hypothesis?

A  null hypothesis  is a hypothesis when there is no significant relationship between the dependent and the participants’ independent  variables . 

In simple words, it’s a hypothesis that has been put forth but hasn’t been proved as yet. A researcher aims to disprove the theory. The abbreviation “Ho” is used to denote a null hypothesis.

If you want to compare two methods and assume that both methods are equally good, this assumption is considered the null hypothesis.

Example: In an automobile trial, you feel that the new vehicle’s mileage is similar to the previous model of the car, on average. You can write it as: Ho: there is no difference between the mileage of both vehicles. If your findings don’t support your hypothesis and you get opposite results, this outcome will be considered an alternative hypothesis.

If you assume that one method is better than another method, then it’s considered an alternative hypothesis. The alternative hypothesis is the theory that a researcher seeks to prove and is typically denoted by H1 or HA.

If you support a null hypothesis, it means you’re not supporting the alternative hypothesis. Similarly, if you reject a null hypothesis, it means you are recommending the alternative hypothesis.

Example: In an automobile trial, you feel that the new vehicle’s mileage is better than the previous model of the vehicle. You can write it as; Ha: the two vehicles have different mileage. On average/ the fuel consumption of the new vehicle model is better than the previous model.

If a null hypothesis is rejected during the hypothesis test, even if it’s true, then it is considered as a type-I error. On the other hand, if you don’t dismiss a hypothesis, even if it’s false because you could not identify its falseness, it’s considered a type-II error.

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How to Conduct Hypothesis Testing?

Here is a step-by-step guide on how to conduct hypothesis testing.

Step 1: State the Null and Alternative Hypothesis

Once you develop a research hypothesis, it’s important to state it is as a Null hypothesis (Ho) and an Alternative hypothesis (Ha) to test it statistically.

A null hypothesis is a preferred choice as it provides the opportunity to test the theory. In contrast, you can accept the alternative hypothesis when the null hypothesis has been rejected.

Example: You want to identify a relationship between obesity of men and women and the modern living style. You develop a hypothesis that women, on average, gain weight quickly compared to men. Then you write it as: Ho: Women, on average, don’t gain weight quickly compared to men. Ha: Women, on average, gain weight quickly compared to men.

Step 2: Data Collection

Hypothesis testing follows the statistical method, and statistics are all about data. It’s challenging to gather complete information about a specific population you want to study. You need to  gather the data  obtained through a large number of samples from a specific population. 

Example: Suppose you want to test the difference in the rate of obesity between men and women. You should include an equal number of men and women in your sample. Then investigate various aspects such as their lifestyle, eating patterns and profession, and any other variables that may influence average weight. You should also determine your study’s scope, whether it applies to a specific group of population or worldwide population. You can use available information from various places, countries, and regions.

Step 3: Select Appropriate Statistical Test

There are many  types of statistical tests , but we discuss the most two common types below, such as One-sided and two-sided tests.

Note: Your choice of the type of test depends on the purpose of your study 

One-sided Test

In the one-sided test, the values of rejecting a null hypothesis are located in one tail of the probability distribution. The set of values is less or higher than the critical value of the test. It is also called a one-tailed test of significance.

Example: If you want to test that all mangoes in a basket are ripe. You can write it as: Ho: All mangoes in the basket, on average, are ripe. If you find all ripe mangoes in the basket, the null hypothesis you developed will be true.

Two-sided Test

In the two-sided test, the values of rejecting a null hypothesis are located on both tails of the probability distribution. The set of values is less or higher than the first critical value of the test and higher than the second critical value test. It is also called a two-tailed test of significance. 

Example: Nothing can be explicitly said whether all mangoes are ripe in the basket. If you reject the null hypothesis (Ho: All mangoes in the basket, on average, are ripe), then it means all mangoes in the basket are not likely to be ripe. A few mangoes could be raw as well.

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Step 4: Select the Level of Significance

When you reject a null hypothesis, even if it’s true during a statistical hypothesis, it is considered the  significance level . It is the probability of a type one error. The significance should be as minimum as possible to avoid the type-I error, which is considered severe and should be avoided. 

If the significance level is minimum, then it prevents the researchers from false claims. 

The significance level is denoted by  P,  and it has given the value of 0.05 (P=0.05)

If the P-Value is less than 0.05, then the difference will be significant. If the P-value is higher than 0.05, then the difference is non-significant.

Example: Suppose you apply a one-sided test to test whether women gain weight quickly compared to men. You get to know about the average weight between men and women and the factors promoting weight gain.

Step 5: Find out Whether the Null Hypothesis is Rejected or Supported

After conducting a statistical test, you should identify whether your null hypothesis is rejected or accepted based on the test results. It would help if you observed the P-value for this.

Example: If you find the P-value of your test is less than 0.5/5%, then you need to reject your null hypothesis (Ho: Women, on average, don’t gain weight quickly compared to men). On the other hand, if a null hypothesis is rejected, then it means the alternative hypothesis might be true (Ha: Women, on average, gain weight quickly compared to men. If you find your test’s P-value is above 0.5/5%, then it means your null hypothesis is true.

Step 6: Present the Outcomes of your Study

The final step is to present the  outcomes of your study . You need to ensure whether you have met the objectives of your research or not. 

In the discussion section and  conclusion , you can present your findings by using supporting evidence and conclude whether your null hypothesis was rejected or supported.

In the result section, you can summarise your study’s outcomes, including the average difference and P-value of the two groups.

If we talk about the findings, our study your results will be as follows:

Example: In the study of identifying whether women gain weight quickly compared to men, we found the P-value is less than 0.5. Hence, we can reject the null hypothesis (Ho: Women, on average, don’t gain weight quickly than men) and conclude that women may likely gain weight quickly than men.

Did you know in your academic paper you should not mention whether you have accepted or rejected the null hypothesis? 

Always remember that you either conclude to reject Ho in favor of Haor   do not reject Ho . It would help if you never rejected  Ha  or even  accept Ha .

Suppose your null hypothesis is rejected in the hypothesis testing. If you conclude  reject Ho in favor of Haor   do not reject Ho,  then it doesn’t mean that the null hypothesis is true. It only means that there is a lack of evidence against Ho in favour of Ha. If your null hypothesis is not true, then the alternative hypothesis is likely to be true.

Example: We found that the P-value is less than 0.5. Hence, we can conclude reject Ho in favour of Ha (Ho: Women, on average, don’t gain weight quickly than men) reject Ho in favour of Ha. However, rejected in favour of Ha means (Ha: women may likely to gain weight quickly than men)

Frequently Asked Questions

What are the 3 types of hypothesis test.

The 3 types of hypothesis tests are:

• One-Sample Test : Compare sample data to a known population value.
• Two-Sample Test : Compare means between two sample groups.
• ANOVA : Analyze variance among multiple groups to determine significant differences.

What is a hypothesis?

A hypothesis is a proposed explanation or prediction about a phenomenon, often based on observations. It serves as a starting point for research or experimentation, providing a testable statement that can either be supported or refuted through data and analysis. In essence, it’s an educated guess that drives scientific inquiry.

What are null hypothesis?

A null hypothesis (often denoted as H0) suggests that there is no effect or difference in a study or experiment. It represents a default position or status quo. Statistical tests evaluate data to determine if there’s enough evidence to reject this null hypothesis.

What is the probability value?

The probability value, or p-value, is a measure used in statistics to determine the significance of an observed effect. It indicates the probability of obtaining the observed results, or more extreme, if the null hypothesis were true. A small p-value (typically <0.05) suggests evidence against the null hypothesis, warranting its rejection.

What is p value?

The p-value is a fundamental concept in statistical hypothesis testing. It represents the probability of observing a test statistic as extreme, or more so, than the one calculated from sample data, assuming the null hypothesis is true. A low p-value suggests evidence against the null, possibly justifying its rejection.

What is a t test?

A t-test is a statistical test used to compare the means of two groups. It determines if observed differences between the groups are statistically significant or if they likely occurred by chance. Commonly applied in research, there are different t-tests, including independent, paired, and one-sample, tailored to various data scenarios.

When to reject null hypothesis?

Reject the null hypothesis when the test statistic falls into a predefined rejection region or when the p-value is less than the chosen significance level (commonly 0.05). This suggests that the observed data is unlikely under the null hypothesis, indicating evidence for the alternative hypothesis. Always consider the study’s context.

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Home » Data Collection – Methods Types and Examples

Data Collection – Methods Types and Examples

Table of Contents

Data collection is a critical step in the research process, involving gathering information to analyze, interpret, and make informed conclusions. Data collection methods vary depending on the research goals, study design, and resources available, and may include quantitative or qualitative techniques. Effective data collection ensures accurate, reliable, and valid data that supports meaningful insights.

Data Collection

Data collection is the systematic process of gathering information from various sources to answer research questions, test hypotheses, and evaluate outcomes. It involves selecting the right method to obtain relevant data for a specific study. Proper data collection is essential for the credibility and validity of research findings.

Types of Data in Research

Data can be broadly classified into two categories: Primary Data and Secondary Data .

• Primary Data : Data collected directly by the researcher for the first time, tailored specifically to the study’s objectives. Examples include surveys, interviews, and experiments.
• Secondary Data : Data collected previously by other researchers or institutions, used for purposes different from its original collection. Examples include government reports, historical records, and previously published studies.

Data can also be divided into Quantitative Data (numerical data, which can be measured and analyzed statistically) and Qualitative Data (non-numerical data, which provides descriptive information about experiences, feelings, and opinions).

Types of Data Collection Methods

1. surveys and questionnaires.

• Definition : Surveys and questionnaires involve asking a set of questions to a target audience, either in person, by mail, online, or over the phone.
• Types : Surveys can be structured (fixed response options) or unstructured (open-ended responses).
• Advantages : Cost-effective, efficient for collecting large amounts of data.
• Example : A customer satisfaction survey to assess user experience with a product.

2. Interviews

• Definition : Interviews are a qualitative data collection method involving direct interaction between the interviewer and respondent. They can be structured, semi-structured, or unstructured, depending on the study.
• Types : Structured (fixed questions), semi-structured (some flexibility), unstructured (free-flowing conversation).
• Advantages : Provides in-depth insights, allows for probing questions.
• Example : An interview with a healthcare provider to understand patient challenges and care practices.

3. Focus Groups

• Definition : Focus groups are a qualitative data collection method involving a small group discussion guided by a moderator to gather participants’ views on a specific topic.
• Advantages : Offers diverse perspectives, helps identify trends or common themes.
• Example : Conducting a focus group with parents to discuss concerns about remote learning.

4. Observations

• Definition : Observation involves systematically watching and recording behavior, events, or conditions as they occur in natural or controlled settings.
• Types : Participant observation (researcher actively engages) and non-participant observation (researcher does not interact).
• Advantages : Provides contextually rich data, ideal for studying behaviors in real-time.
• Example : Observing classroom dynamics to assess teacher-student interaction.

5. Experiments

• Definition : Experiments are controlled studies where researchers manipulate one or more variables to observe their effect on other variables. This method is common in scientific and psychological research.
• Advantages : Provides causal relationships, highly controlled and replicable.
• Example : Testing the effects of a new drug on patient recovery rates in a clinical trial.

6. Case Studies

• Definition : A case study is an in-depth investigation of a single instance or group, exploring complex issues within real-life contexts. It is commonly used in social sciences, psychology, and business.
• Advantages : Provides detailed, contextualized insights.
• Example : Conducting a case study on a successful business to understand its growth strategies.

7. Document and Content Analysis

• Definition : This method involves analyzing existing documents, texts, images, or media content to identify patterns, trends, and meanings.
• Advantages : Useful for studying historical or pre-existing data, non-intrusive.
• Example : Analyzing news articles to study media portrayal of climate change.

8. Online Data Collection (Digital Tools)

• Definition : Online data collection methods include using digital tools, platforms, and analytics software to gather data from online activities, behaviors, and interactions.
• Advantages : Efficient for collecting data from large and diverse audiences, especially for web-based studies.
• Example : Collecting data on consumer preferences through website analytics.

Choosing the Right Data Collection Method

Selecting a data collection method depends on several factors:

• Research Objectives : Whether the study aims to test hypotheses, describe a phenomenon, or explore a topic in depth.
• Type of Data : Quantitative or qualitative, depending on the data’s nature and how it will be analyzed.
• Resources and Budget : The availability of time, finances, and resources.
• Population and Sample Size : Whether the study involves a large population or a specific, smaller group.
• Reliability and Validity : Ensuring that the chosen method produces accurate, consistent, and valid data.

Examples of Data Collection in Different Fields

• Surveys : Collecting feedback from students on course satisfaction.
• Observation : Observing classroom interactions to assess teaching methods.
• Case Studies : Studying the impact of specific teaching strategies on student performance.
• Interviews : Conducting interviews with patients to understand healthcare needs.
• Experiments : Running clinical trials to test new treatments.
• Focus Groups : Gathering healthcare providers’ perspectives on a new policy.
• Online Data Collection : Analyzing website analytics to understand customer behavior.
• Surveys : Conducting customer satisfaction surveys post-purchase.
• Content Analysis : Examining social media posts to study brand reputation.
• Experiments : Testing theories on behavior through controlled experiments.
• Case Studies : Investigating a rare psychological condition in detail.
• Focus Groups : Understanding group dynamics or attitudes towards mental health interventions.

Data collection is a fundamental part of research that provides the foundation for analysis and interpretation. Choosing the appropriate data collection method is essential for obtaining accurate, reliable, and relevant information. Whether through surveys, experiments, or online tools, the chosen method should align with the study’s objectives, type of data needed, and resources available. With effective data collection, researchers can draw meaningful conclusions and make valuable contributions to their field of study.

• Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . SAGE Publications.
• Kumar, R. (2018). Research Methodology: A Step-by-Step Guide for Beginners . SAGE Publications.
• Flick, U. (2014). An Introduction to Qualitative Research . SAGE Publications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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