实验数据的排列测试。Permutation tests for experimental data.-医云文献数字医云科研云海量医学决策数据服务

Abstract：

This article surveys the use of nonparametric permutation tests for analyzing experimental data. The permutation approach, which involves randomizing or permuting features of the observed data, is a flexible way to draw statistical inferences in common experimental settings. It is particularly valuable when few independent observations are available, a frequent occurrence in controlled experiments in economics and other social sciences. The permutation method constitutes a comprehensive approach to statistical inference. In two-treatment testing, permutation concepts underlie popular rank-based tests, like the Wilcoxon and Mann-Whitney tests. But permutation reasoning is not limited to ordinal contexts. Analogous tests can be constructed from the permutation of measured observations-as opposed to rank-transformed observations-and we argue that these tests should often be preferred. Permutation tests can also be used with multiple treatments, with ordered hypothesized effects, and with complex data-structures, such as hypothesis testing in the presence of nuisance variables. Drawing examples from the experimental economics literature, we illustrate how permutation testing solves common challenges. Our aim is to help experimenters move beyond the handful of overused tests in play today and to instead see permutation testing as a flexible framework for statistical inference.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s10683-023-09799-6.

摘要：

本文调查了使用非参数置换测试来分析实验数据。置换方法，这涉及随机化或置换观察数据的特征，是在常见实验设置中得出统计推断的灵活方法。当很少有独立的观察可用时，这是特别有价值的，在经济学和其他社会科学的对照实验中经常发生。置换方法构成了统计推断的综合方法。在两次治疗测试中，排列概念是流行的基于等级的测试的基础，比如Wilcoxon和Mann-Whitney的测试.但是置换推理并不限于序数上下文。可以根据测量的观察结果的排列来构建类似的测试-与秩变换的观察结果相反-我们认为这些测试通常应该是首选。置换测试也可以与多种治疗一起使用，有有序的假设效应，复杂的数据结构，例如在存在干扰变量的情况下进行假设检验。借鉴实验经济学文献中的例子，我们说明了置换测试如何解决常见的挑战。我们的目标是帮助实验者超越当今过度使用的测试，而是将置换测试视为统计推断的灵活框架。
■在线版本包含补充材料，可在10.1007/s10683-023-09799-6获得。