What is the purpose of hypothesis testing?

Hypothesis testing serves a critical role in statistical analysis by allowing you to make informed decisions based on sample data while evaluating two competing statements: the null hypothesis and the alternative hypothesis. The null hypothesis typically represents a general statement of no effect or no difference, while the alternative hypothesis reflects the research question or the effect you are testing for.

In this context, hypothesis testing involves several steps, including formulating hypotheses, selecting a significance level, calculating a test statistic, and comparing the results to critical values or p-values to either reject or fail to reject the null hypothesis. This process enables researchers to assess whether observed data provide enough evidence to support a specific claim or to reject a default position.

While determining if an event is likely or unlikely can be a part of making conclusions in hypothesis testing, the primary focus lies on the structured decision-making process involving the evaluation of both hypotheses. Therefore, the purpose of hypothesis testing is best summarized by its ability to guide decision-making using empirical data, rather than simply accepting a hypothesis or listing possible outcomes.