Neyman-Pearson Framework

The Neyman-Pearson framework is a statistical method used for hypothesis testing. It helps to decide whether to accept or reject a hypothesis based on data. The framework focuses on balancing two types of errors: a false positive (incorrectly rejecting a true hypothesis) and a false negative (failing to reject a false hypothesis). By establishing a specific probability threshold for error acceptance, it enables researchers to optimize their decisions based on the context, ensuring they are making informed conclusions while minimizing the risks of errors in testing their hypotheses.