The Neyman-Pearson framework is a statistical approach used for hypothesis testing. It helps researchers decide between two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1). This framework emphasizes controlling the probability of making errors, specifically the Type I error, which occurs when H0 is incorrectly rejected. In this framework, a critical value is determined based on a predefined significance level (α). If the test statistic exceeds this critical value, H0 is rejected in favor of H1. This method is particularly useful in fields like biostatistics and quality control, where making accurate decisions is crucial.