Abstract

Researchers are encouraged to report effect size statistics to quantify treatment effects or effects due to group differences. However, estimates of effect sizes, most commonly Cohen’s d, make assumptions about the distribution of data that are not always true. An alternative nonparametric estimate of effect size, relying on the median absolute deviation, is proposed. Comparison of this estimate to Cohen’s d using (simulated) non-normally distributed data demonstrate that the nonparametric approach effect size may be a better estimate of effect size.

中文翻译：

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简要研究报告：关于效应大小的非参数估计的注释

摘要

鼓励研究人员报告效应量统计数据，以量化治疗效果或由于群体差异而产生的效果。然而，效应大小的估计，最常见的是 Cohen 的d，对并不总是正确的数据分布做出假设。提出了一种依赖于中值绝对偏差的效应大小的替代非参数估计。使用（模拟的）非正态分布数据将此估计与 Cohen 的d进行比较表明，非参数方法效应大小可能是效应大小的更好估计。