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皮尔逊贝叶斯因子的闭式近似方法，该因子是近期发展的一种用于评估两分组设计中数据证据价值的指标。这些技术依赖于关于伽马函数的一些经典渐近结果。在仅能获得最小摘要统计量（即t分数与自由度）的情况下，这些近似方法允许对皮尔逊贝叶斯因子进行简单的闭式计算。此外，在近似实验设计中的贝叶斯因子时，这些方法的性能显著优于经典的BIC方法。