This article discusses multiple hypothesis testing in the context of DNA microarray experiments, where the goal is to identify differentially expressed genes. It reviews basic concepts and procedures for multiple testing, including Type I and Type II error rates, and introduces different approaches such as the Bonferroni, Šidák, Holm, and Hochberg procedures. The article also explores the false discovery rate (FDR) as an alternative to the family-wise error rate (FWER) and discusses resampling methods like permutation and bootstrap for estimating p-values. The methods are evaluated using microarray and simulated data sets, highlighting the trade-offs between FWER and FDR control in the context of large-scale hypothesis testing.This article discusses multiple hypothesis testing in the context of DNA microarray experiments, where the goal is to identify differentially expressed genes. It reviews basic concepts and procedures for multiple testing, including Type I and Type II error rates, and introduces different approaches such as the Bonferroni, Šidák, Holm, and Hochberg procedures. The article also explores the false discovery rate (FDR) as an alternative to the family-wise error rate (FWER) and discusses resampling methods like permutation and bootstrap for estimating p-values. The methods are evaluated using microarray and simulated data sets, highlighting the trade-offs between FWER and FDR control in the context of large-scale hypothesis testing.