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Most scientific disciplines use significance testing to draw conclusionsabout experimental or observational data. This classical approach provides atheoretical guarantee for controlling the number of false positives across aset of hypothesis tests, making it an appealing framework for scientistsseeking to limit the number of false effects or associations that they claim toobserve. Unfortunately, this theoretical guarantee applies to few experiments,and the true false positive rate (FPR) is much higher. Scientists have plentyof freedom to choose the error rate to control, the tests to include in theadjustment, and the method of correction, making strong error control difficultto attain. In addition, hypotheses are often tested after finding unexpectedrelationships or patterns, the data are analysed in several ways, and analysesmay be run repeatedly as data accumulate. As a result, adjusted p-values aretoo small, incorrect conclusions are often reached, and results are harder toreproduce. In the following, I argue why the FPR is rarely controlledmeaningfully and why shrinking parameter estimates is preferable to p-valueadjustments.

Why multiple hypothesis test corrections provide poor control of false positives in the real world