Bayesian hypothesis testing procedures have gained increased acceptance in recent years. A key advantage that Bayesian tests have over classical testing procedures is their potential to quantify information in support of true null hypotheses. Ironically, default implementations of Bayesian tests prevent the accumulation of strong evidence in favor of true null hypotheses because associated default alternative hypotheses assign a high probability to data that are most consistent with a null effect. We propose the use of “nonlocal” alternative hypotheses to resolve this paradox. The resulting class of Bayesian hypothesis tests permits more rapid accumulation of evidence in favor of both true null hypotheses and alternative hypotheses that are compatible with standardized effect sizes of most interest in psychology.

中文翻译：

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心理学中贝叶斯假设检验的有效替代方案。

近年来，贝叶斯假设检验程序得到了越来越多的认可。贝叶斯检验相对于经典检验程序的一个关键优势是它们可以量化信息以支持真实的原假设。具有讽刺意味的是，贝叶斯检验的默认实现阻止了支持真实零假设的强有力证据的积累，因为相关的默认替代假设将高概率分配给与零效应最一致的数据。我们建议使用“非局部”替代假设来解决这个悖论。由此产生的贝叶斯假设检验类别可以更快地积累证据，支持真零假设和与心理学最感兴趣的标准化效应大小兼容的替代假设。