Multiple Testing Problem

This lecture covers the multiple testing problem in genomic data analysis, focusing on the challenges of simultaneously testing multiple null hypotheses. It discusses the concepts of Type I and Type II errors, as well as various methods for controlling error rates, such as Bonferroni, Sidak, and Holm procedures. The lecture also explores the importance of adjusted p-values and the control of false discovery rates. Additionally, it delves into the application of permutation tests, advantages, limitations, and pitfalls in hypothesis testing, emphasizing the need for careful interpretation of statistical significance in genomic studies.