Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.
Behav Res Methods. 2024 Dec;56(8):8552-8566. doi: 10.3758/s13428-024-02491-4. Epub 2024 Sep 4.
Bayes factor hypothesis testing provides a powerful framework for assessing the evidence in favor of competing hypotheses. To obtain Bayes factors, statisticians often require advanced, non-standard tools, making it important to confirm that the methodology is computationally sound. This paper seeks to validate Bayes factor calculations by applying two theorems attributed to Alan Turing and Jack Good. The procedure entails simulating data sets under two hypotheses, calculating Bayes factors, and assessing whether their expected values align with theoretical expectations. We illustrate this method with an ANOVA example and a network psychometrics application, demonstrating its efficacy in detecting calculation errors and confirming the computational correctness of the Bayes factor results. This structured validation approach aims to provide researchers with a tool to enhance the credibility of Bayes factor hypothesis testing, fostering more robust and trustworthy scientific inferences.
贝叶斯因子假设检验为评估竞争假设的证据提供了一个强大的框架。为了获得贝叶斯因子，统计学家通常需要先进的、非标准的工具，因此确认该方法在计算上是合理的非常重要。本文旨在通过应用艾伦·图灵和杰克·古德的两个定理来验证贝叶斯因子的计算。该程序包括在两个假设下模拟数据集，计算贝叶斯因子，并评估它们的期望值是否与理论期望一致。我们通过一个方差分析示例和一个网络心理计量学应用来说明这种方法，证明了它在检测计算错误和确认贝叶斯因子结果的计算正确性方面的有效性。这种结构化的验证方法旨在为研究人员提供一种工具，以提高贝叶斯因子假设检验的可信度，促进更稳健和可靠的科学推断。
