Multiple Hypothesis Testing

This lecture covers the challenges of conducting multiple tests simultaneously, focusing on the need to control the error rate when dealing with a large number of tests. The instructor explains various methods for combining evidence from multiple tests to avoid false positives, such as the Bonferroni method, Holm's method, and the Benjamini-Hochberg procedure. The lecture also delves into the concept of false discovery rate and its significance in minimizing the proportion of false positives among all positives in hypothesis testing. Additionally, non-parametric estimation techniques are discussed, emphasizing the construction of empirical distribution functions to estimate distributions without assuming specific parametric forms.