对p-hacking鲁棒的临界值
Critical Values Robust to P-hacking
AI总结：
本文提出了一种对p-hacking鲁棒的临界值方法，通过考虑科学家的p-hacking行为调整，使显著结果频率符合预期，同时临界值比传统方法更大。
摘要：
p-hacking在现实中很普遍，但在经典假设检验理论中却不存在。因此，当原假设实际上为真时，显著结果比预期更常见。在本文中，我们构建了一个包含p-hacking的假设检验模型。从该模型中，我们构建出临界值，使得如果这些值用于确定显著性，并且科学家的p-hacking行为适应于新的显著性标准，那么显著结果将出现预期的频率。这种鲁棒的临界值允许p-hacking，因此比经典临界值更大。为了说明p-hacking可能需要的修正量，我们使用医学科学的证据校准模型。在校准后的模型中，任何检验统计量的鲁棒临界值是相同检验统计量的经典临界值，其显著性水平减少五分之一。
英文摘要：
P-hacking is prevalent in reality but absent from classical hypothesis testing theory. As a consequence, significant results are much more common than they are supposed to be when the null hypothesis is in fact true. In this paper, we build a model of hypothesis testing with p-hacking. From the model, we construct critical values such that, if the values are used to determine significance, and if scientists' p-hacking behavior adjusts to the new significance standards, significant results occur with the desired frequency. Such robust critical values allow for p-hacking so they are larger than classical critical values. To illustrate the amount of correction that p-hacking might require, we calibrate the model using evidence from the medical sciences. In the calibrated model the robust critical value for any test statistic is the classical critical value for the same test statistic with one fifth of the significance level.
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