Invalid Permutation Tests
نویسنده
چکیده
Permutation tests are often presented in a rather casual manner, in both introductory and advanced statistics textbooks. The appeal of the cleverness of the procedure seems to replace the need for a rigorous argument that it produces valid hypothesis tests. The consequence of this educational failing has been a widespread belief in a “permutation principle”, which is supposed invariably to give tests that are valid by construction, under an absolute minimum of statistical assumptions. Several lines of argument are presented here to show that the permutation principle itself can be invalid, concentrating on the Fisher-Pitman permutation test for two means. A simple counterfactual example illustrates the general problem, and a slightly more elaborate counterfactual argument is used to explain why the main mathematical proof of the validity of permutation tests is mistaken. Two modifications of the permutation test are suggested to be valid in a very modest simulation. In instances where simulation software is readily available, investigating the validity of a specific permutation test can be done easily, requiring only a minimum understanding of statistical technicalities.
منابع مشابه
Comments on the analysis of unbalanced microarray data
MOTIVATION Permutation testing is very popular for analyzing microarray data to identify differentially expressed (DE) genes; estimating false discovery rates (FDRs) is a very popular way to address the inherent multiple testing problem. However, combining these approaches may be problematic when sample sizes are unequal. RESULTS With unbalanced data, permutation tests may not be suitable bec...
متن کاملPermutation testing in the presence of polygenic variation.
This article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution--for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gen...
متن کاملStatistical tests for fMRI based on experimental randomization.
Statistical parametric mapping (SPM) analysis of fMRI data requires specifying correctly the temporal and spatial noise covariance structure. This is a difficult if not impossible task. When these assumptions are not satisfied, statistical inference can be invalid or inefficient. Permutation tests are free of strong assumptions on the distribution of signal noise. We propose permutation tests o...
متن کاملGlobal permutation tests for multivariate ordinal data: alternatives, test statistics, and the null dilemma
We discuss two-sample global permutation tests for sets of multivariate ordinal data in possibly high-dimensional setups, motivated by the analysis of data collected by means of the World Health Organisation’s International Classification of Functioning, Disability and Health. The tests do not require any modelling of the multivariate dependence structure. Specifically, we consider testing for ...
متن کاملCommentary: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. Fo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Int. J. Math. Mathematical Sciences
دوره 2010 شماره
صفحات -
تاریخ انتشار 2010