The Neyman-Pearson framework is a statistical approach used in hypothesis testing. It helps researchers decide between two competing hypotheses: the null hypothesis and the alternative hypothesis. The goal is to maximize the probability of correctly rejecting the null hypothesis when it is false, while controlling the probability of making a Type I error (false positive). This method introduces the concept of the Neyman-Pearson lemma, which provides a criterion for determining the most powerful test for a given significance level. By comparing the likelihood ratios of the two hypotheses, researchers can make informed decisions based on their data.