发表机构
University of Wisconsin-Madison; University of Iowa（威斯康星大学麦迪逊分校; 爱荷华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究p-hacking对选择性发表论文估计值偏倚的影响，发现快速p-hacking加剧偏倚、缓慢p-hacking依选择力度不同有不同作用，将模型应用于两类元分析得到相关暗示性证据。
AI 中文摘要
本文研究当具有统计显著结果的论文被选择性发表时，p-hacking（p值操纵）对已发表估计值偏倚的影响。我们表明，快速p-hacking（导致p值发生较大变化的操作）总会加剧选择性发表带来的偏倚；而缓慢p-hacking（导致p值发生较小变化的操作）在选择力度弱时会加剧偏倚，在选择力度强时则会缓解偏倚。在同时包含两种p-hacking的模型中，我们证明正态性假设可识别真实的效应分布，以及无p-hacking时选择性发表下的反事实均值。将该模型应用于行为助推和发展援助效应的元分析，我们发现实践中同时存在缓解和加剧情况的暗示性证据。
英文摘要
This paper studies the effects of p-hacking on the bias of published estimates when papers with statistically significant results are selectively published. We show that fast p-hacking---actions that lead to large changes in p-values---always exacerbates the bias from selective publication. On the other hand, slow p-hacking---actions that lead to small changes in p-values---exacerbates bias when selection is weak, but mitigates it when selection is strong. In a model featuring both types of p-hacking, we show that a normality assumption identifies the true distribution of effects as well as the counterfactual mean that would obtain under selective publication without p-hacking. Applying the model to meta-analyses on the effects of behavioral nudges and development aid, we find suggestive evidence that both mitigation and exacerbation can arise in practice.
