  Most scientific disciplines use significance testing to draw conclusions
about experimental or observational data. This classical approach provides a
theoretical guarantee for controlling the number of false positives across a
set of hypothesis tests, making it an appealing framework for scientists
seeking to limit the number of false effects or associations that they claim to
observe. Unfortunately, this theoretical guarantee applies to few experiments,
and the true false positive rate (FPR) is much higher. Scientists have plenty
of freedom to choose the error rate to control, the tests to include in the
adjustment, and the method of correction, making strong error control difficult
to attain. In addition, hypotheses are often tested after finding unexpected
relationships or patterns, the data are analysed in several ways, and analyses
may be run repeatedly as data accumulate. As a result, adjusted p-values are
too small, incorrect conclusions are often reached, and results are harder to
reproduce. In the following, I argue why the FPR is rarely controlled
meaningfully and why shrinking parameter estimates is preferable to p-value
adjustments.
