CRITICAL VALUES ROBUST TO P-HACKING

• 成果类型: Article
• 署名作者: McCloskey, Adam; Michaillat, Pascal
• 署名单位: University of Colorado System; University of Colorado Boulder; University of California System; University of California Santa Cruz
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01456
• 发表日期: 2026
• 关键词: publication bias; follow-up; randomized-trials; clinical-research; TRANSPARENCY; Protocols; science; fate

摘要

P-hacking is prevalent in reality but absent from classical hypothesis-testing theory. We therefore build a model of hypothesis testing that accounts for p-hacking. From the model, we derive critical values such that, if they are used to determine significance, and if p-hacking adjusts to the new significance standards, then spurious significant results do not occur more often than intended. Because of p-hacking, such robust critical values are larger than classical critical values. In the model calibrated to medical science, the robust critical value is the classical critical value for the same test statistic but with one-fifth of the significance level.