False discovery rate
False discovery rate (FDR) control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons. In a list of rejected hypotheses, FDR controls the expected proportion of incorrectly rejected null hypotheses (type I errors). [1] It is a less conservative procedure for comparison, with greater power than familywise error rate (FWER) control, at a cost of increasing the likelihood of obtaining type I errors.[2]
In practical terms, the FDR is the expected false positive rate; for example, if 1000 observations were experimentally predicted to be different, and a maximum FDR for these observations was 0.10, then 100 of these observations would be expected to be false positives.
The q value is defined to be the FDR analogue of the p-value. The q-value of an individual hypothesis test is the minimum FDR at which the test may be called significant. One approach is to directly estimate q-values rather than fixing a level at which to control the FDR.
In practical terms, the FDR is the expected false positive rate; for example, if 1000 observations were experimentally predicted to be different, and a maximum FDR for these observations was 0.10, then 100 of these observations would be expected to be false positives.
The q value is defined to be the FDR analogue of the p-value. The q-value of an individual hypothesis test is the minimum FDR at which the test may be called significant. One approach is to directly estimate q-values rather than fixing a level at which to control the FDR.
