Normal Distribution
nothing is called 95% until it has been counted
Concept

False discovery rate — where it appears

The expected share of the claims made that are false, as distinct from the chance of making any false claim. Controlling it is a weaker requirement than controlling the familywise rate and is the right one when the findings are a list to be followed up.

Named by 5 essays across one field — each of them below, with the objects they name alongside it.

Twenty tests, 10 of them real — what each procedure holds. no correction: familywise 40.8%, false discovery 5.3%, power 85%. Bonferroni: familywise 2.8%, false discovery 0.5%, power 49%. Holm: familywise 3.6%, false discovery 0.6%, power 53%. Benjamini–Hochberg: familywise 20.0%, false discovery 2.6%, power 75%.

Two different promises

Bonferroni bounds the chance of any false positive. Benjamini–Hochberg bounds the share of the findings that are false. Both are called correcting for multiple comparisons, and one of them lets the familywise rate reach 20%.

multiplicity · Multiplicity Power to find a real effect of 3 standard errors, 10 of 20 real. no correction finds 85.1%, Bonferroni finds 49.1%, Holm finds 52.5%, Benjamini–Hochberg finds 74.9%. The uncorrected procedure finds the most and controls nothing.

The price of control

Every correction is paid for in power, and the exchange rate can be measured. Holm buys familywise control for 33 percentage points of power; Benjamini–Hochberg buys a weaker guarantee for 10. Neither is free and neither is a matter of taste.

multiplicity · Multiplicity The false discovery rate of twenty correlated tests, against the correlation. BH, every null true: 5.08% at 0, 4.86% at 0.3, 3.70% at 0.6, 2.34% at 0.9. BH, 10 of 20 real: 2.55% at 0, 2.53% at 0.3, 2.26% at 0.6, 1.66% at 0.9. BY, every null true: 1.46% at 0, 1.31% at 0.3, 1.03% at 0.6, 0.69% at 0.9. BY, 10 of 20 real: 0.72% at 0, 0.75% at 0.3, 0.64% at 0.6, 0.50% at 0.9. 20,000 families at each correlation.

False discoveries that arrive together

Correlate twenty tests and Benjamini–Hochberg still holds its false discovery rate — 1.66% at a correlation of 0.9 with ten real effects, against 2.55% when the tests are independent. What changes is how the errors come. A family of true nulls reports anything 2.34% of the time instead of 5.08%, and when it does, it reports 16.56 false findings out of twenty.

multiplicity · Multiplicity Storey's estimate of the share of true nulls over twenty thousand families, independent and correlated at 0.6. The true share is 0.5. Independent tests: mean 0.610, spread 0.160, below half the truth in 0.92% of families. Correlated at 0.6: mean 0.609, spread 0.240, below half the truth in 9.33%.

Estimating how many nulls are true

Benjamini–Hochberg at 5% delivers 2.55% when half of twenty nulls are false, because it cannot tell how many are. Storey's estimate of that share, read off the p-values above one half, spends the rest and finds 81.93% of the real effects instead of 74.70% on independent tests. Correlated at 0.9, the same procedure reports a finding in 19.29% of families in which every null is true.

multiplicity · Multiplicity Every finding Benjamini–Hochberg made in thirty families, with its interval, at real effects of 2. 85 findings, sorted by their estimate. 12 of their ordinary 95% intervals miss the true effect, every one of them on the far side; 6 of the wider false-coverage-rate intervals miss.

Intervals for the findings

Benjamini–Hochberg's findings usually go out each with its ordinary 95% interval. With ten real effects of two standard errors among twenty tests, 11.59% of those intervals miss their effect, every miss on the far side, and the interval around the most prominent finding covers 72.36% of the time — 2.38% when the effects are one standard error. Intervals widened for the number of findings hold the share that miss under 5%.

multiplicity · Multiplicity

Named alongside it

The objects these essays reach for when they reach for this one.

Multiple comparisonsBenjamini–HochbergDependenceError rateFalse positiveHolmMonte CarloStatistical powerBonferroniCorrelationEffect sizeFamilywise error rate

All concepts