论文标题
需要提出T统计障碍吗？
Do t-Statistic Hurdles Need to be Raised?
论文作者
论文摘要
许多学者呼吁提出统计障碍，以防止学术出版物中的虚假发现。我表明这些呼叫可能很难以经验证明。已发表的数据展示偏见：无法满足现有障碍的结果通常无法观察到。这些未观察到的结果必须推断出来，这可能导致对修订的障碍的识别较弱。相比之下，可以强烈识别仅针对已发表的发现（例如经验贝叶斯收缩和FDR）的统计数据，因为关于已发表的发现的数据很丰富。我从理论上和对横截面返回可预测性文献的经验分析中证明了这些结果。
Many scholars have called for raising statistical hurdles to guard against false discoveries in academic publications. I show these calls may be difficult to justify empirically. Published data exhibit bias: results that fail to meet existing hurdles are often unobserved. These unobserved results must be extrapolated, which can lead to weak identification of revised hurdles. In contrast, statistics that can target only published findings (e.g. empirical Bayes shrinkage and the FDR) can be strongly identified, as data on published findings is plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature.
