When conducting hypothesis tests, for example, to see whether two means are significantly different, we calculate a p-value, which is the probability of obtaining a test statistic that is as or more extreme than the observed one, assuming the null hypothesis is true. When we are conducting multiple comparisons (I will call each test a “feature”), we have an increased probability of false positives. The more features you have, the higher the chances of a null feature being called significant. The false-positive rate (FPR), or per comparison error rate (PCER), is the expected number of false positives out of all hypothesis tests conducted. Controlling for the false discovery rate (FDR) is a way to identify as many significant features as possible while incurring a relatively low proportion of false positives. Learn more: https://www.publichealth.columbia.edu/research/population-health-methods/false-discovery-rate