In this paper, I present three closed-form approximations of the two-sample Pearson Bayes factor, a recently developed index of evidential value for data in two-group designs. The techniques rely on some classical asymptotic results about Gamma functions. These approximations permit simple closed-form calculation of the Pearson Bayes factor in cases where only minimal summary statistics are available (i.e., the t-score and degrees of freedom). Moreover, these approximations vastly outperform the classic BIC method for approximating Bayes factors from experimental designs.

翻译：本文提出了三种针对两样本Pearson贝叶斯因子的闭式近似方法，该因子是近期发展的用于评估双组设计数据证据价值的指标。这些技术依赖于Gamma函数的一些经典渐近结果。在仅能获得最小化汇总统计量（即t分数与自由度）的情况下，这些近似方法允许通过简单闭式形式计算Pearson贝叶斯因子。此外，在实验设计的贝叶斯因子近似任务中，这些方法的表现显著优于经典的BIC方法。