The Benjamini-Hochberg Procedure (FDR) And P-Value Adjusted Explained
How to calculate the P-Value Adjusted in R, Python and Excel
In this tutorial, we will show you how to apply the Benjamini-Hochberg procedure in order to calculate the False Discovery Rate (FDR) and the P-Value Adjusted.
The Benjamini-Hochberg procedure, also known as the False Discovery Rate (FDR) procedure, is a statistical method used in multiple hypothesis testing to control the expected proportion of false discoveries. In many scientific studies or experiments, researchers test multiple hypotheses simultaneously, but when multiple tests are performed, the probability of obtaining at least one false positive result increases leading to an increased overall Type I error rate.
The Benjamini-Hochberg procedure addresses this issue by controlling the FDR, which is defined as the expected proportion of false positives among the rejected hypotheses.
The calculation of adjusted p-values in the Benjamini-Hochberg procedure involves comparing each individual p-value to a critical value or threshold. The critical value is determined based on the desired false discovery rate (FDR) control.
Here are the steps involved in calculating adjusted p-values using the Benjamini-Hochberg procedure:
