🤖 AI Summary
This study addresses the inflation of false positive rates in multiple hypothesis testing by systematically reviewing error rate control criteria—such as the family-wise error rate (FWER) and the false discovery rate (FDR)—and integrating both classical and contemporary correction methods. The work provides reproducible implementations of these approaches in R, offering a theoretically rigorous yet practice-oriented resource for teaching and applied research. By unifying methodological foundations with hands-on computational examples, this contribution fills a critical gap in existing textbooks, which often lack comprehensive integration of techniques and practical guidance. The resulting framework serves as a complete and efficient reference for graduate-level instruction and real-world data analysis, enhancing both pedagogical clarity and analytical reliability in high-dimensional statistical inference.
📝 Abstract
This text provides an introduction to multiple hypothesis testing. It covers various error criteria and testing procedures, and includes references to relevant R packages. An earlier version of this text served as the lecture notes for a PhD-level course on multiple testing.