Process of Hypothesis Testing in Research: Why & How?

Hypothesis testing is an essential process in research that is used to figure out the significance of a research hypothesis. The hypothesis testing process involves a series of steps that researchers must follow to ensure that their results are reliable and valid.

In this article, we will explain how to test a hypothesis and why researchers must conduct this process correctly.

What is the purpose of a hypothesis test?

The primary purpose why you should test your hypothesis is to figure out whether there is enough evidence to support or reject a research hypothesis.

A research hypothesis is a statement about the relationship between two or more variables. The hypothesis test helps researchers to figure out the validity of their hypothesis and the quantitative value of their results.

By conducting hypothesis testing, researchers can make informed decisions about their research findings.

Why is hypothesis testing important?

Hypothesis testing is essential for several reasons. Firstly, it provides researchers with a framework to figure out whether the results of their research are quantitatively substantial or just due to chance. Secondly, it helps researchers to identify the strengths and weaknesses of their research methodology, ensuring that the research is conducted accurately and reliably. Thirdly, it provides a means to evaluate competing theories and hypotheses.

How should you test a hypothesis?

The process of hypothesis testing involves several steps, including stating the null and alternative hypotheses, determining the significance level, choosing the appropriate quantitative test, collecting and analyzing data, and interpreting the results.

These steps include:

  1. State your null and alternative hypotheses

The first step in the hypothesis testing process is to state your null and alternative hypotheses. A null hypothesis is a statement with no substantial difference between the variables being tested. In contrast, the alternative hypothesis is the statement with a substantial difference between the tested variables.

  1. Figure out the level of significance

The level of significance is the probability of obtaining a result that is as extreme or more extreme than the observed result, assuming the null hypothesis is true.

  1. Choose the appropriate quantitative test

The quantitative test you choose will depend on the type of data you have and the research question you are trying to answer. Various quantitative tests can be used, such as t-tests, ANOVA, and regression analysis.

  1. Collect and analyze your data

The next step is to collect your data and analyze it using the appropriate quantitative test. This involves calculating the test statistic and the p-value.

  1. Interpret your results

The final step is to interpret your results. If the p-value is less than the level of significance, you can reject the null hypothesis and accept the alternative hypothesis. If the p-value is more significant than the level of significance, you cannot reject the null hypothesis.

Terms to be used in hypothesis

The procedure to test hypothesis is a vital component of the research process. It helps to ensure that the results obtained from research are reliable and valid. But to correctly do this, you must familiarise yourself with standard terms.

Below are some critical terminologies you must know:

  • Type I and Type II errors

Hypothesis testing involves the risk of making the Type I and the Type II errors. Type I errors happen when the null hypothesis is rejected but it is true. In contrast, Type II errors happen when the null hypothesis is not rejected but it is indeed false. Researchers need to be aware of the potential for these errors and take steps to minimize them.

  • Sample amount

The amount of the sample used in the research can affect the results of the hypothesis test. An enormous sample amount generally provides more accurate results, while a smaller sample amount can lead to more variability and uncertainty in the results.

  • Power analysis

Power analysis is a quantitative method that can be used to figure out the sample amount needed to achieve a certain level of quantitative power. Quantitative power is the probability of detecting a substantial difference between groups when one exists. 


By following these steps, researchers can ensure that their results are valid and reliable. Also, they can make informed decisions based on their research findings.

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