The Importance of Random Slopes in Mixed Models for Bayesian Hypothesis Testing
通过五个模拟实验，发现贝叶斯混合模型中忽略随机斜率会导致假阳性结论大幅增加，而包含随机斜率虽降低检测真效应的能力，但能有效控制假阳性风险。
Mixed models are gaining popularity in psychology. For frequentist mixed models, previous research showed that excluding random slopes-differences between individuals in the direction and size of an effect-from a model when they are in the data can lead to a substantial increase in false-positive conclusions in null-hypothesis tests. Here, I demonstrated through five simulations that the same is true for Bayesian hypothesis testing with mixed models, which often yield Bayes factors reflecting very strong evidence for a mean effect on the population level even if there was no such effect. Including random slopes in the model largely eliminates the risk of strong false positives but reduces the chance of obtaining strong evidence for true effects. I recommend starting analysis by testing the support for random slopes in the data and removing them from the models only if there is clear evidence against them.