An Introduction to Bayesian Hypothesis Testing for Management Research
介绍贝叶斯假设检验和贝叶斯因子作为p值显著性检验的替代方法，能量化支持零假设的证据且无需调整数据收集意图，并通过分层回归实例展示p值高估反对零假设证据的问题。
In management research, empirical data are often analyzed using p-value null hypothesis significance testing (pNHST). Here we outline the conceptual and practical advantages of an alternative analysis method: Bayesian hypothesis testing and model selection using the Bayes factor. In contrast to pNHST, Bayes factors allow researchers to quantify evidence in favor of the null hypothesis. Also, Bayes factors do not require adjustment for the intention with which the data were collected. The use of Bayes factors is demonstrated through an extended example for hierarchical regression based on the design of an experiment recently published in the Journal of Management. This example also highlights the fact that p values overestimate the evidence against the null hypothesis, misleading researchers into believing that their findings are more reliable than is warranted by the data.