Should we always choose a nonparametric test when comparing two apparently nonnormal distributions?
Should we always choose a nonparametric test when comparing two apparently nonnormal distributions?
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DOI：
10.1016/s0895-4356(00)00264-x
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发表时间：
2001-01-01
影响因子：
7.2
通讯作者：
Fenstad, GU
中科院分区：
文献类型：
--
作者：
Skovlund, E;Fenstad, GU
在对临床数据进行统计分析时，一个常见的问题是如何选择合适的显著性检验。将两个独立的组与连续测量的观察值进行比较，问题通常是选择两样本t检验还是Wilcoxon-Mann-Whitney检验(WMW检验)。经常得到类似的结果，但得出的结论是。如果显着性检验得出的P值差异很大，是否可以得出结论？对于具有共同方差的正态分布观测值，t检验是最优的，如果样本量不是很小，则对偏离正态分布具有稳健性。WMW检验不做任何分布假设，但在很大程度上依赖于两个分布的相等形状和方差(同方差)。我们比较了传统的两样本t检验、允许不等方差的修正t检验和随机模拟的WMW检验的性质。当两种分布具有相似的方差时，它们都表现出可接受的行为，当方差不同时，改进的t检验优于另外两种。(C)2001 Elsevier Science Inc.保留所有权利。
When clinical data are subjected to statistical analysis, a common question is how to choose an appropriate significance test. Comparing two independent groups with observations measured on a continuous scale, the question is typically whether to choose the two-sample-t test or the Wilcoxon-Mann-Whitney test (WMW test). Similar results are often obtained, but which conclusion. can be drawn if significance tests give highly different P-values? The t test is optimal for normally distributed observations with common variance and robust to deviations from normality if sample sizes are not very small. The WMW test makes no distributional assumptions, but depends heavily on equal shape and variance of the two distributions (homoscedasticity). We have compared the properties of the traditional two-sample t test, a modified t test allowing unequal variance, and the WMW test by stochastic simulation. All show acceptable behaviour when the two distributions have similar variance, When variances differ, the modified t test is superior to the other two. (C) 2001 Elsevier Science Inc. All rights reserved.