Bayes factor analysis becomes increasingly popular, among other reasons, because it allows to provide evidence for the null hypothesis, which is not easily possible with the traditional frequentist approach. A conceivable strategy that apparently takes favorable aspects of both approaches on board is to use traditional frequentist analyses first and to support theoretically interesting nil effects by Bayesian analyses thereafter. Here, we asked whether such a selective application of Bayesian analyses to only nonsignificant effects of foregoing frequentist analyses creates bias. In two simulation studies, we observed that such selective application of Bayesian analyses, in fact, severely overestimates evidence in favor of the null hypotheses, when a true population effect exists. While this bias can be attenuated by using more informative priors in the Bayesian analyses, we recommend to not apply such selective combination of analytical approaches, but instead to use either frequentist or Bayesian analyses consistently. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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防止选择性应用贝叶斯因子的警告说明。


贝叶斯因子分析变得越来越流行，除其他原因外，因为它允许为零假设提供证据，这在传统的频率论方法中是不容易实现的。一个显然考虑了这两种方法的有利方面的可想而知的策略是首先使用传统的频率主义分析，然后通过贝叶斯分析来支持理论上有趣的零效应。在这里，我们询问了将贝叶斯分析选择性地应用于上述频率主义分析的不显著影响是否会产生偏倚。在两项模拟研究中，我们观察到，当存在真正的总体效应时，这种对贝叶斯分析的选择性应用实际上严重高估了支持零假设的证据。虽然可以通过在贝叶斯分析中使用信息量更大的先验来减轻这种偏倚，但我们建议不要应用这种选择性的分析方法组合，而是始终如一地使用频率分析或贝叶斯分析。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。