为什么多重假设检验校正在现实世界中对假阳性的控制很差
Why multiple hypothesis test corrections provide poor control of false positives in the real world
摘要：
大多数科学学科都使用显著性检验来对实验或观察数据得出结论。这种经典方法为控制一组假设检验中的假阳性数量提供了理论保证，使其成为寻求限制他们声称观察到的假效应或关联数量的科学家的一个有吸引力的框架。不幸的是，这种理论保证适用于很少的实验，并且真假阳性率（FPR）要高得多。科学家们有很大的自由度来选择要控制的错误率、要包括在调整中的测试以及校正方法，这使得很难实现强大的错误控制。此外，在发现意外的关系或模式后，通常会对假设进行测试，数据会以多种方式进行分析，并且随着数据的积累，分析可能会重复运行。因此，调整后的p值太小，经常得出不正确的结论，结果更难重现。在下文中，我将讨论为什么FPR很少被有意义地控制，以及为什么收缩参数估计比p值调整更可取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
