Schreiner Marcel R, Kunde Wilfried
Department of Psychology, Julius-Maximilians-Universitat Wurzburg.
J Exp Psychol Gen. 2024 Oct 7. doi: 10.1037/xge0001666.
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).
贝叶斯因子分析越来越受欢迎，原因之一是它能够为零假设提供证据，而这在传统的频率主义方法中并不容易实现。一种显然融合了两种方法优点的可行策略是，先进行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。在此，我们探讨了这种仅对先前频率主义分析中的非显著效应进行贝叶斯分析的选择性应用是否会产生偏差。在两项模拟研究中，我们观察到，当总体真实效应存在时，这种对贝叶斯分析的选择性应用实际上会严重高估支持零假设的证据。虽然在贝叶斯分析中使用信息性更强的先验可以减弱这种偏差，但我们建议不要采用这种分析方法的选择性组合，而是始终如一地使用频率主义或贝叶斯分析。（PsycInfo数据库记录（c）2024美国心理学会，保留所有权利）
