BMC Medical Research Methodology | |
t-tests, non-parametric tests, and large studies—a paradox of statistical practice? | |
关键词: T-test; Non-parametric test; Wilcoxon-Mann-Whitney test; Welch test; Sample size; Statistical practice; | |
DOI : 10.1186/1471-2288-12-78 | |
来源: DOAJ |
【 摘 要 】
Abstract
Background
During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences.
Methods
A simulation study is used to compare the rejection rates of the Wilcoxon-Mann-Whitney (WMW) test and the two-sample t-test for increasing sample size. Samples are drawn from skewed distributions with equal means and medians but with a small difference in spread. A hypothetical case study is used for illustration and motivation.
Results
The WMW test produces, on average, smaller
Conclusions
Non-parametric tests are most useful for small studies. Using non-parametric tests in large studies may provide answers to the wrong question, thus confusing readers. For studies with a large sample size, t-tests and their corresponding confidence intervals can and should be used even for heavily skewed data.
【 授权许可】
Unknown