What is the main purpose of hypothesis testing in statistics?

Hypothesis testing is a fundamental procedure in statistics that allows researchers to make inferences about a population based on sample data. The main purpose is to evaluate the strength of evidence against a null hypothesis in order to determine whether there is sufficient statistical support to accept an alternative hypothesis.

In this process, a specific claim or statement about a population (such as a mean, proportion, or relationship between variables) is formally tested. By comparing sample data to what is expected under the null hypothesis, researchers can calculate p-values or confidence intervals that indicate whether observed effects are statistically significant, thus supporting or refuting their claims about the population.

This methodology is essential in fields such as business, medicine, and social sciences, where decision-making often relies on empirical evidence rather than assumptions. It enables a structured approach to evaluating hypotheses and helps mitigate biases in the interpretation of results. Therefore, the purpose of hypothesis testing is centered around this critical evaluation of evidence to support claims about a population.