We study a statistical framework for replicability based on a recently proposed quantitative measure of replication success, the sceptical $p$-value. A recalibration is proposed to obtain exact overall Type-I error control if the effect is null in both studies and additional bounds on the partial and conditional Type-I error rate, which represent the case where only one study has a null effect. The approach avoids the double dichotomization for significance of the two-trials rule and has larger project power to detect existing effects over both studies in combination. It can also be used for power calculations and requires a smaller replication sample size than the two-trials rule for already convincing original studies. We illustrate the performance of the proposed methodology in an application to data from the Experimental Economics Replication Project.

翻译：我们根据最近提出的复制成功定量衡量标准研究可复制性的统计框架,即怀疑值$-价值,建议进行重新校正,以获得准确的总体类型I错误控制,如果两项研究的结果均无效,并且对部分和有条件类型I错误率附加限制,即只有一项研究具有无效效果,这种办法避免了两审判规则的双重二分法,并具有较大的项目能力,可同时探测两项研究的现有效果,还可以用于动力计算,而且对于已经具有说服力的原始研究,需要比二审规则更小的复制样本规模。我们举例说明了在应用实验经济学复制项目数据方面拟议方法的绩效。