In 1995, statisticians Yoav Benjamini and Yosef Hochberg developed a method to limit these false positives. It controls the false discovery rate, or FDR, which is the share of reported significant results that are actually false alarms.
Correlated data can make the method miss its target
The Benjamini-Hochberg procedure, or BH, is now widely used in modern statistics and across many scientific fields. According to Edgar Dobriban, an associate professor at the University of Pennsylvania's Wharton School, the original paper has received more than 130,000 citations.
Benjamini and Hochberg originally showed that their method works with independent data. Real-world data points, however, are often linked. Genetic variants can be correlated, for example, when certain locations in the genome are frequently inherited together.
For years, experts assumed the BH procedure would also work reliably with correlated, normally distributed data, specifically when testing for deviations in both directions. But nobody had ever proved it.
