Hypothesis Testing in Research | Step By Step Guide With Example

Hypothesis Testing for Research – Complete Guide With Example

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Hypothesis Testing Research

Hypothesis testing is a statistical technique used for deciding whether to reject the null hypothesis. Hypothesis testing helps researchers know whether or not they are on the correct path for their research project and can save them from time wasted pursuing wrong leads. Before starting any type of hypothesis test, it is vital that you have an idea of what your variable will be and how to measure it accurately. This post will cover some of the most common types of hypothesis tests and provide examples, as well as discuss what they are designed to test.

What is a hypothesis in research?

Hypothesis in research is basically a statement that helps you to define the relationship between different types of variables for your study. You can consider the hypothesis as the expectation about the things that can happen during research. The main objective of including hypotheses in research is to get the answer to research questions.

For example, while watering a plant you expect that if you will give more amount of water and sunlight to the plant, it would grow big soon.

What are the different types of hypothesis?

There are different types of hypothesis but here, we will discuss mainly two types of hypothesis, these are :

  • Null hypothesis: It is a hypothesis, where you can not expect variations. The null hypothesis states that there is no relationship between the dependent and independent variables. For instance, there is no significant relationship between compensation policy and employee satisfaction. In research null hypothesis is denoted as H0.
  • Alternative hypothesis: In the Alternative hypothesis, variations are expected. This is something that a researcher aims to indirectly verify by stating their assumption, which states that there exists a significant linkage between population parameters. For instance, there is a significant relationship between companies’ compensation policy and employee satisfaction. It denoted as Ha or H1.

What is hypothesis testing?

Hypothesis testing is basically a statistical procedure that is researcher performs with the purpose of determining whether there are chances of a specific hypothesis to be true. In simple words, by using the statistics you can tests whether your prediction about the population parameters is correct or not.

Or we can say, Hypothesis testing is a research method that uses statistical tools to prove or disprove a theory. The hypothesis is an idea about what might be true, and the goal of hypothesis testing is to provide evidence for or against that idea.

What are the uses of hypothesis testing?

  • You can use hypothesis testing for making assumptions about the outcome of the hypothesis on sample information that you have gathered from a large population.
  • Students can utilize it for analyzing the strong proof which you have collected from the sample.
  • Hypothesis testing provides the structure for making various assumptions about the population.

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How to conduct hypothesis testing?

The test procedure or the rule is based upon a test statistic and a rejection region. The process of testing the hypothesis consists of the following steps:

Step 1 – Specification of hypothesis

It is the first step in hypothesis testing where you need to clearly define the null and alternative hypotheses. While stating the hypothesis you need to make sure that it is mutually exclusive which means that if one statement is true then the other should be false. While defining the variables you need to confirm that the statement representing the relationship between two or more variables.

Step 2 – Selection of significance level

This is a stage where you need to set the significance level. The significance level (denoted by the Greek letter alpha— a) is generally set at 0.05. This means that there is a 5% chance that you will accept your alternative hypothesis when your null hypothesis is actually true. The smaller the significance level, the greater the burden of proof needed to reject the null hypothesis, or in other words, to support the alternative hypothesis.

Step 3 – Collection of information

It is a stage where you will require accumulating all the facts about the research topic. The process of making statistical inferences should be done in a way that is designed to test your hypothesis. If you do not design the sampling methods and data collection appropriately, then it will make no sense for you to try drawing conclusions about any population at all!

Step 4 – Determination of critical values

This is a phase where you need to determine P-value. It is basically a value that the researcher utilizes for determining statistical importance in hypothesis tests. Determination of P-value is important as it will help you in evaluating the extent up to which the hypothesis statement given by you is true. It will also help you in analyzing the extent up to which the facts which you have gathered are compatible with the null hypothesis.

There are basically two types of P-value these are :

  1. High: High P-value indicates that the facts which you have collected are highly compatible with the null hypothesis.
  2. Low: It is the P-value that represents that the information which the researcher has to accumulate is not at all compatible with the null hypothesis.

Comparison of critical value and making a judgment: Here, you need to make a comparison between P values, and on the basis of the same you need to make a decision.

Step 5 – Drawing a conclusion

With the conclusion stage, we either accept or reject the null hypothesis. The decision is based on computed values of the test statistics and whether it lies in the acceptance region or rejection region respectively.
If the computed value of the test statistic falls in the acceptance region (it means the computed value is less than the critical value), the null hypothesis is accepted. On the contrary, if the computed value of the test statistic is greater than the critical value, the computed value of the statistic falls in the rejection region, and the null hypothesis is rejected.

Hypothesis Testing Example

A manager in pipe manufacturing needs to ensure that the diameters of pipes manufactured by the machine are 5 cm. Then you as a manager is needed to undergo the following phases of the hypothesis test these are:

  1. Establishment for criteria: At this stage, you as a manager will need to design the hypothesis. The null hypothesis here could be every pipe has a diameter of 5 cm. AS manager needs to confirm that every pipe should be diameter 5 cm then he can make a selection from an alternative hypothesis which could be The population mean is fewer than the target, the mean of the population which manager has select is greater in comparison to the target. A third alternative hypothesis could be men population is completely different from the target.  In such a case, the manager can select two side alternative hypotheses. An alternative hypothesis which manager could select is the mean population of all pipes is not 5 cm.
  2. Selection of significance level: At this stage, you can select the most basically used significance level which is 0.05.
  3. Collection of facts: It is a stage where you will require gathering information about pipes and their diameters.
  4. Comparison between P values: After completion of the hypothesis test you will obtain P-value. Suppose here the P-value which results in 0.04 which is less than the significance level that is 0.05. on the basis of comparison between P values, you need to make a decision whether to reject the null hypothesis or not.

Read Also: Thematic Analysis for Research

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