The False Discovery Rate (FDR) is a statistical measure used primarily in the context of multiple hypothesis testing. It quantifies the expected proportion of incorrect rejections (false positives) among all positive findings (both true and false positives). In simpler terms, when researchers conduct multiple tests simultaneously, FDR helps them understand how many of their significant results might actually be false discoveries.
FDR is particularly important in fields like genomics, where thousands of hypotheses may be tested at once, making it crucial to control for the likelihood of false positives. For example, if a researcher identifies 100 genes as significantly associated with a condition, but 20 of those findings are false positives, the FDR would be 20%. This knowledge can guide decisions on which results to trust and pursue further.
To control for FDR, techniques such as the Benjamini-Hochberg procedure are often employed. These methods adjust the significance thresholds based on the number of comparisons made, thereby balancing the rate of false discoveries with the desire to identify true effects. Understanding and managing the FDR is essential for rigorous scientific research and helps to improve the reliability of conclusions drawn from data analysis.