Assessing the Effect of Multiple Comparison Corrections on False Discovery Rate Control in Statistical Testing

Posted: Jan 08, 2004

• Arthur Simmons
• Ashley Diaz
• Audrey Barnes

Abstract

The proliferation of high-dimensional data across scientific disciplines has fundamentally transformed the landscape of statistical inference, necessitating simultaneous testing of numerous hypotheses. This paradigm shift has elevated the importance of multiple comparison corrections from a statistical nuance to a fundamental component of rigorous scientific practice. While the theoretical foundations of multiple testing corrections have been extensively developed over the past several decades, the practical implications of method selection on false discovery rate control remain inadequately characterized. The false discovery rate, defined as the expected proportion of false positives among all rejected hypotheses, has emerged as a particularly valuable error metric in large-scale testing scenarios where some false discoveries are acceptable in exchange for increased power.