When analyzing data researchers make some decisions that are either arbitrary, based on subjective beliefs about the data generating process, or for which equally justifiable alternative choices could have been made. This wide range of data-analytic choices can be abused, and has been one of the underlying causes of the replication crisis in several fields. Recently, the introduction of multiverse analysis provides researchers with a method to evaluate the stability of the results across reasonable choices that could be made when analyzing data. Multiverse analysis is confined to a descriptive role, lacking a proper and comprehensive inferential procedure. Recently, specification curve analysis adds an inferential procedure to multiverse analysis, but this approach is limited to simple cases related to the linear model, and only allows researchers to infer whether at least one specification rejects the null hypothesis, but not which specifications should be selected. In this paper we present a Post-selection Inference approach to Multiverse Analysis (PIMA) which is a flexible and general inferential approach that accounts for all possible models, i.e., the multiverse of reasonable analyses. The approach allows for a wide range of data specifications (i.e. pre-processing) and any generalized linear model; it allows testing the null hypothesis of a given predictor not being associated with the outcome, by merging information from all reasonable models of multiverse analysis, and provides strong control of the family-wise error rate such that it allows researchers to claim that the null-hypothesis can be rejected for each specification that shows a significant effect. The inferential proposal is based on a conditional resampling procedure. To be continued...

翻译：在数据分析过程中，研究者会做出一些决策，这些决策或基于对数据生成过程的主观信念而具有随意性，或存在其他同样合理的替代选择。这种广泛的数据分析选择可能被滥用，并已成为多个领域复制危机的原因之一。近年来，多重宇宙分析的引入为研究者提供了一种评估不同合理分析选择下结果稳定性的方法。然而，多重宇宙分析仅限于描述性作用，缺乏适当且全面的推断程序。近期，规范曲线分析为多重宇宙分析增加了推断程序，但该方法局限于与线性模型相关的简单情形，仅允许研究者推断是否至少有一种规范拒绝原假设，而无法确定应选择哪些规范。本文提出一种针对多重宇宙分析的选择后推断方法（PIMA），这是一种灵活且通用的推断方法，能够考虑所有可能的模型，即合理分析的多重宇宙。该方法适用于广泛的数据规范（即预处理）和任何广义线性模型；通过整合多重宇宙分析中所有合理模型的信息，检验给定预测变量与结果之间无关联的原假设，并提供了对族系误差率的强控制，从而使研究者能够宣称：对于每个显示显著效应的规范，原假设均可被拒绝。该推断方法基于条件重抽样过程。（待续...）