论文标题
意义过滤器，获胜者的诅咒和缩水的需求
The Significance Filter, the Winner's Curse and the Need to Shrink
论文作者
论文摘要
“显着性过滤器”是指专门关注统计学意义的结果。由于仅在观察到数据之前，诸如无偏见和覆盖范围之类的常见特性是有效的，因此，如果我们有意义，则无法保证。实际上，显着性滤波器导致高估参数的大小，这被称为“赢家的诅咒”。它还可以导致置信区间的底层底层。此外，如果功率低，这些问题将变得更加严重。尽管这些问题显然值得我们注意，但仅对它们进行了非正式研究，并且缺乏数学结果。在这里，我们从频繁主义者和贝叶斯的角度研究它们。我们证明，大小的相对偏置是功率的降低功能，并且当功率小于50％时，通常的置信区间秘密秘密。我们得出的结论是，未能应用适当的收缩量会导致误导性的推论。
The "significance filter" refers to focusing exclusively on statistically significant results. Since frequentist properties such as unbiasedness and coverage are valid only before the data have been observed, there are no guarantees if we condition on significance. In fact, the significance filter leads to overestimation of the magnitude of the parameter, which has been called the "winner's curse". It can also lead to undercoverage of the confidence interval. Moreover, these problems become more severe if the power is low. While these issues clearly deserve our attention, they have been studied only informally and mathematical results are lacking. Here we study them from the frequentist and the Bayesian perspective. We prove that the relative bias of the magnitude is a decreasing function of the power and that the usual confidence interval undercovers when the power is less than 50%. We conclude that failure to apply the appropriate amount of shrinkage can lead to misleading inferences.
