Veillette John P, Nusbaum Howard C
Department of Psychology, University of Chicago, Chicago, USA.
Commun Psychol. 2025 Jan 23;3(1):9. doi: 10.1038/s44271-025-00190-0.
Much research in the behavioral sciences aims to characterize the "typical" person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.
行为科学中的许多研究旨在刻画“典型”的人。具有统计学意义的组平均效应量通常被解释为典型的人表现出某种效应的证据，但这仅在某些分布假设下才成立，而对于这些假设，很少有明确的证据。平均效应量会随着参与者内部效应量和总体患病率（表现出效应的人群比例）而变化。很少有研究考虑患病率如何影响平均效应量估计，相反，现有的患病率估计方法会因效应量的不确定性而混淆。我们引入了一种广泛适用的贝叶斯方法——p曲线混合模型，该模型通过基于零分布下的可能性对参与者层面的数据进行概率聚类，联合估计患病率和效应量。我们提供了一个软件工具来实现我们的方法，当效应量不确定时，该方法优于现有的患病率估计方法，并且对不同组或条件下的患病率或效应量差异敏感。
