A Complete Review of Controlling the FDR in a Multiple Comparison Problem Framework -- The Benjamini-Hochberg Algorithm

TL;DR

This paper reviews the Benjamini-Hochberg method and related FDR control techniques, emphasizing their theoretical foundations and practical advantages in multiple hypothesis testing scenarios.

Contribution

It provides a comprehensive, accessible review of FDR controlling methods, highlighting the significance of the Benjamini-Hochberg procedure and its impact on statistical testing.

Findings

Benjamini-Hochberg method effectively controls FDR in multiple testing.

The procedure offers increased power over traditional FWER methods.

FDR control is more suitable for large-scale testing problems.

Abstract

This paper is a review of the popular Benjamini Hochberg Method and other related useful methods of Multiple Hypothesis testing. This is written with the purpose of serving a short but complete easy to understand review of the main article with proper background. The paper titled 'Controlling the False Discovery Rate-a practical and powerful Approach to multiple Testing' by benjamini et. al.[1] proposes a new framework of controlling the False Discovery Rate in a Multiple Hypothesis testing problem. It has been claimed that the procedure proposed in the paper results in a substantial gain in power more applicable in case of problems which call for False discovery rate (FDR) control rather than Familywise Error Rate (FWER). The proposed method uses a simple Bonferroni type procedure for FDR control.