Neyman-Pearson hypothesis testing

Neyman-Pearson hypothesis testing is a method used to decide between two competing claims about data—called hypotheses—by setting a threshold for how unlikely the data must be to reject a claim. It involves defining a “null hypothesis” (no effect or difference) and an “alternative hypothesis” (there is an effect). The test calculates the probability that observed data would occur if the null hypothesis is true. If this probability is very low (below a predetermined level), the null hypothesis is rejected in favor of the alternative, balancing the risks of false positives and false negatives to make an informed decision.