{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T21:32:54Z","timestamp":1783027974493,"version":"3.54.6"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Permutation test is a popular technique for testing a hypothesis of no effect, when the distribution of the test statistic is unknown. To test the equality of two means, a permutation test might use a test statistic which is the difference of the two sample means in the univariate case. In the multivariate case, it might use a test statistic which is the maximum of the univariate test statistics. A permutation test then estimates the null distribution of the test statistic by permuting the observations between the two samples.<\/jats:p>\n               <jats:p>We will show that, for such tests, if the two distributions are not identical (as for example when they have unequal variances, correlations or skewness), then a permutation test for equality of means based on difference of sample means can have an inflated Type I error rate even when the means are equal. Our results illustrate permutation testing should be confined to testing for non-identical distributions.<\/jats:p>\n               <jats:p>Contact: \u00a0calian@raunvis.hi.is<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl383","type":"journal-article","created":{"date-parts":[[2006,7,27]],"date-time":"2006-07-27T00:58:50Z","timestamp":1153961930000},"page":"2244-2248","source":"Crossref","is-referenced-by-count":69,"title":["To permute or not to permute"],"prefix":"10.1093","volume":"22","author":[{"given":"Yifan","family":"Huang","sequence":"first","affiliation":[{"name":"H. Lee Moffitt Cancer Center & Research Institute, The University of South Florida 1 \u00a0 1 \u00a0 \u00a0 Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Clinical Biostatistics, Johnson & Johnson Pharmaceutical Research & Development 2 \u00a0 2 \u00a0 \u00a0 L.L.C., USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Violeta","family":"Calian","sequence":"additional","affiliation":[{"name":"Science Institute, University of Iceland 3 \u00a0 3 \u00a0 \u00a0 Dunhaga 3, 107 Reykjavik, Iceland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason C.","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Ohio State University 4 \u00a0 4 \u00a0 \u00a0 Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2006,7,26]]},"reference":[{"key":"2023012409205532600_b1","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbl023","article-title":"Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity","volume-title":"em Technical Report 771","author":"Hsu","year":"2006"},{"key":"2023012409205532600_b2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1093\/bioinformatics\/bth469","article-title":"Outcome signature genes in breast cancer: is there a unique set?","volume":"21","author":"Ein-Dor","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012409205532600_b3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/S0167-7152(97)00043-6","article-title":"Studentized permutation tests for non - i.i.d. hypotheses and the generalized Behrens-Fisher","volume":"36","author":"Janssen","year":"1997","journal-title":"Stat. 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