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当可能时,报告 Fisher 精确检验值并展示其潜在的零分布。

When possible, report a Fisher-exact value and display its underlying null randomization distribution.

机构信息

Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA 02138;

Yau Center for Mathematical Sciences, Tsinghua University, Beijing 100084, China.

出版信息

Proc Natl Acad Sci U S A. 2020 Aug 11;117(32):19151-19158. doi: 10.1073/pnas.1915454117. Epub 2020 Jul 23.

Abstract

In randomized experiments, Fisher-exact values are available and should be used to help evaluate results rather than the more commonly reported asymptotic values. One reason is that using the latter can effectively alter the question being addressed by including irrelevant distributional assumptions. The Fisherian statistical framework, proposed in 1925, calculates a value in a randomized experiment by using the actual randomization procedure that led to the observed data. Here, we illustrate this Fisherian framework in a crossover randomized experiment. First, we consider the first period of the experiment and analyze its data as a completely randomized experiment, ignoring the second period; then, we consider both periods. For each analysis, we focus on 10 outcomes that illustrate important differences between the asymptotic and Fisher tests for the null hypothesis of no ozone effect. For some outcomes, the traditional value based on the approximating asymptotic Student's distribution substantially subceeded the minimum attainable Fisher-exact value. For the other outcomes, the Fisher-exact null randomization distribution substantially differed from the bell-shaped one assumed by the asymptotic test. Our conclusions: When researchers choose to report values in randomized experiments, 1) Fisher-exact values should be used, especially in studies with small sample sizes, and 2) the shape of the actual null randomization distribution should be examined for the recondite scientific insights it may reveal.

摘要

在随机实验中,Fisher 精确值是可用的,应该用于帮助评估结果,而不是更常用的渐近值。原因之一是,使用后者实际上可以通过包含不相关的分布假设来改变正在解决的问题。Fisher 统计框架于 1925 年提出,通过使用导致观察数据的实际随机化程序来计算随机实验中的值。在这里,我们在交叉随机实验中说明了这种 Fisherian 框架。首先,我们考虑实验的第一期,并将其数据作为完全随机实验进行分析,忽略第二期;然后,我们同时考虑两期。对于每种分析,我们都关注 10 种结果,这些结果说明了 Fisher 检验和渐近检验在零假设无臭氧效应方面的重要差异。对于一些结果,基于近似渐近学生分布的传统值大大低于最小可达 Fisher 精确值。对于其他结果,Fisher 精确的零随机化分布与渐近检验所假设的钟形分布有很大的不同。我们的结论是:当研究人员选择在随机实验中报告值时,1)应使用 Fisher 精确值,尤其是在样本量较小的研究中,2)应检查实际的零随机化分布的形状,以揭示其可能揭示的深奥科学见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/180a/7431075/024c76c4f9c8/pnas.1915454117fig01.jpg

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