To establish a theory one needs cleverly designed and well-executed studies with appropriate and correctly interpreted statistical analyses. Equally important, one also needs replications of such studies and a way to combine the results of several replications into an accumulated state of knowledge. An approach that provides an appropriate and powerful analysis for studies targeting prespecified theories is the use of Bayesian informative hypothesis testing. An additional advantage of the use of this Bayesian approach is that combining the results from multiple studies is straightforward. In this article, we discuss the behavior of Bayes factors in the context of evaluating informative hypotheses with multiple studies. By using simple models and (partly) analytical solutions, we introduce and evaluate Bayesian evidence synthesis (BES) and compare its results to Bayesian sequential updating. By doing so, we clarify how different replications or updating questions can be evaluated. In addition, we illustrate BES with two simulations, in which multiple studies are generated to resemble conceptual replications. The studies in these simulations are too heterogeneous to be aggregated with conventional research synthesis methods. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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信息假设的贝叶斯证据综合：简介。

为了建立一种理论，需要巧妙设计和执行良好的研究，并进行适当和正确解释的统计分析。同样重要的是，人们还需要重复这些研究，并需要一种将多次重复的结果结合成知识积累状态的方法。为针对预先指定的理论的研究提供适当且强大的分析的方法是使用贝叶斯信息假设检验。使用这种贝叶斯方法的另一个优点是可以直接组合多项研究的结果。在本文中，我们在评估多项研究的信息假设的背景下讨论贝叶斯因子的行为。通过使用简单的模型和（部分）分析解决方案，我们介绍和评估贝叶斯证据合成（BES）并将其结果与贝叶斯顺序更新进行比较。通过这样做，我们阐明了如何评估不同的重复或更新问题。此外，我们通过两个模拟来说明 BES，其中生成了多项研究以类似于概念复制。这些模拟中的研究过于异构，无法用传统的研究综合方法进行聚合。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。