What is the main goal of hypothesis testing in statistics?

The main goal of hypothesis testing in statistics is to determine if a population parameter significantly differs from a hypothesized value. This process involves formulating a null hypothesis, which posits that there is no effect or difference, and an alternative hypothesis, which suggests that there is a significant effect or difference. By using sample data, statistical tests are conducted to assess the strength of the evidence against the null hypothesis.

This approach is crucial in establishing whether observed data provide enough evidence to reject the null hypothesis in favor of the alternative. The results of hypothesis testing guide researchers in making informed decisions regarding their hypotheses based on the statistical significance of the results obtained.

Estimating characteristics of a population relates more to inferential statistics rather than specifically to the core aim of hypothesis testing. Similarly, while finding the mean of a sample is a fundamental statistical procedure, it does not directly address the goal of testing hypotheses about parameters. Proving a hypothesis correct is more of a misconception, as hypothesis testing is focused on evidence and statistical significance rather than definitive proof.