Vankov Ivan I
Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Sofia City, Bulgaria.
Front Psychol. 2023 Aug 24;14:1220281. doi: 10.3389/fpsyg.2023.1220281. eCollection 2023.
The presence of outliers in response times can affect statistical analyses and lead to incorrect interpretation of the outcome of a study. Therefore, it is a widely accepted practice to try to minimize the effect of outliers by preprocessing the raw data. There exist numerous methods for handling outliers and researchers are free to choose among them. In this article, we use computer simulations to show that serious problems arise from this flexibility. Choosing between alternative ways for handling outliers can result in the inflation of -values and the distortion of confidence intervals and measures of effect size. Using Bayesian parameter estimation and probability distributions with heavier tails eliminates the need to deal with response times outliers, but at the expense of opening another source of flexibility.
响应时间中异常值的存在会影响统计分析,并导致对研究结果的错误解读。因此,通过对原始数据进行预处理来尽量减少异常值的影响是一种被广泛接受的做法。存在众多处理异常值的方法,研究人员可以自由选择。在本文中,我们通过计算机模拟表明,这种灵活性会引发严重问题。在处理异常值的不同方法之间进行选择可能会导致p值膨胀以及置信区间和效应量指标的扭曲。使用贝叶斯参数估计和具有更厚尾的概率分布无需处理响应时间异常值,但代价是开启了另一个灵活性来源。