Neyman-Pearson Theorem

The Neyman-Pearson theorem is a statistical principle used to make decisions based on data. It helps determine the best way to test a hypothesis, typically in situations where there are two competing claims: one we assume is true (the null hypothesis) and one we suspect might be true (the alternative hypothesis). The theorem provides a method for choosing a testing threshold that minimizes errors—specifically, the chance of incorrectly rejecting the true claim while maximizing the likelihood of correctly identifying the true situation. Essentially, it guides how to balance risks when making statistical decisions.