Statistical hypothesis testing

A formal procedure for deciding whether sample evidence warrants rejection of a null hypothesis (H₀) about a population. A test statistic is computed, its p-value compared to a pre-set significance level α. The Neyman-Pearson framework formalizes the trade-off between Type I error (false rejection) and Type II error (false retention).