Hypothesis Testing in Statistics

What is Hypothesis Testing?

Hypothesis testing is a concept in statistics in which analysts test an assumption regarding a population parameter. It is a method used in statistical inference. The use of hypothesis testing is useful when determining if a statement regarding a population parameter is statistically significant. Overall, it is a critical part of the scientific method, which represents a systematic approach to assessing theories through observation.

Through hypothesis testing, one can assess the plausibility of a hypothesis by using sample data. This data can come from various sources, such as a large population or a process that generates data. Similarly, the hypothesis can come from any judgment used by the user that is achievable. Hypothesis testing is also a great tool for evaluating various scenarios.

How does Hypothesis Testing work?

In hypothesis testing, analysts test a statistical sample to provide evidence on the plausibility of the null hypothesis. To perform hypothesis testing, analysts test a hypothesis by measuring and examining a random sample of the population. They use a random population sample to evaluate two different hypotheses, the null hypothesis, and the alternative hypothesis. The formula for the null hypothesis is as follows.

H0: µ0 = 0

Similarly, the formula for the alternative hypothesis is as follows.

Ha = µ0 ≠ 0

The null hypothesis represents a hypothesis of equality between population parameters. For example, a null hypothesis may state that the population mean return is equal to zero. On the other hand, the alternative hypothesis is the opposite of the null hypothesis, which tests whether the population mean return is not equal to zero.

How to perform Hypothesis Testing?

Hypothesis testing is a several step process. The process starts with analysts stating the null (H0) and the alternative hypothesis (Ha). After that, they consider the statistical assumptions and whether these assumptions are in line with the underlying population in evaluation. After that, analysts determine the appropriate probability distribution and select the appropriate test statistic.

Then, analysts can select the significance level, which is the probability threshold for which they will reject the null hypothesis. Based on the significance level they use and the appropriate test, they must also state the decision rule. From the testing, they can collect the data observed and use it to calculate the test statistic.

After that, analysts should either reject the null hypothesis or fail to reject it based on the results obtained. The process is called a statistical decision. They must also consider any other economic issues that may apply to the problem. These are non-statistical considerations that they must consider for a decision in addition to the statistical decision for a final decision.

As mentioned, hypothesis testing has two parts, the null hypothesis, and the alternative hypothesis. Analysts try to reject the null hypothesis. That is because the alternative hypothesis is often difficult to prove. So, if the null hypothesis is false, then the remaining alternative theory gets accepted.


Hypothesis testing is a method in which analysts test an assumption about a population parameter. There are two parts to hypothesis testing, testing the null and alternative hypotheses. Performing hypothesis is crucial and consists of several steps, as stated above.

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