What does a hypothesis test aim to determine?

A hypothesis test fundamentally aims to assess the strength of evidence against a null hypothesis, which is a standard assumption usually stating that there is no effect or no difference in the population. When conducting a hypothesis test, researchers calculate a test statistic based on sample data and determine the corresponding p-value. If the p-value falls below a predefined significance level, it indicates that the observed data would be highly unlikely under the assumption of the null hypothesis being true. Therefore, the hypothesis test allows researchers to decide whether there is sufficient evidence to reject the null hypothesis, suggesting that an effect or difference may truly exist in the population from which the sample was drawn.

This process distinguishes hypothesis testing as a critical method in inferential statistics, as it allows conclusions to be drawn about populations based on sample data. Rather than simply verifying if the sample is representative, addressing the distribution skewness, or calculating averages, the primary objective remains focused on evaluating the evidence to either support or refute the stated null hypothesis.