论文标题
贝叶斯因子的功能用于报告假设检验的结果
Bayes factor functions for reporting outcomes of hypothesis tests
论文作者
论文摘要
贝叶斯因素代表通过竞争科学假设分配的概率与数据的比率。贝叶斯因素的缺点是它们对先前规格的依赖性，这些规范定义了零和替代假设和计算中遇到的困难。为了解决这些问题，我们直接从共同的测试统计数据定义了贝叶斯因子功能（BFF）。 BFF取决于单个非中心参数，该参数可以作为标准效应大小的函数表示，而BFF与效应大小的图提供了可以在整个研究中易于汇总的假设检验的信息摘要。这样的摘要消除了对定义``统计意义''定义``统计意义''的任意p值阈值的需求。BFF以封闭形式获得，可以从z，t，chi-squared和f统计数据中轻松计算。
Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. Drawbacks of Bayes factors are their dependence on prior specifications that define null and alternative hypotheses and difficulties encountered in their computation. To address these problems, we define Bayes factor functions (BFF) directly from common test statistics. BFFs depend on a single non-centrality parameter that can be expressed as a function of standardized effect sizes, and plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies. Such summaries eliminate the need for arbitrary P-value thresholds to define ``statistical significance.'' BFFs are available in closed form and can be computed easily from z, t, chi-squared, and F statistics.
