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dc.contributor.authorFagerland, Morten W
dc.date.accessioned2015-10-20T10:55:31Z
dc.date.available2015-10-20T10:55:31Z
dc.date.issued2012
dc.identifier.citationBMC Medical Research Methodology. 2012 Jun 14;12(1):78
dc.identifier.urihttp://hdl.handle.net/10852/47146
dc.description.abstractBackground 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 p-values than the t-test. This discrepancy increases with increasing sample size, skewness, and difference in spread. For heavily skewed data, the proportion of p<0.05 with the WMW test can be greater than 90% if the standard deviations differ by 10% and the number of observations is 1000 in each group. The high rejection rates of the WMW test should be interpreted as the power to detect that the probability that a random sample from one of the distributions is less than a random sample from the other distribution is greater than 50%. 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.
dc.language.isoeng
dc.rightsFagerland; licensee BioMed Central Ltd.
dc.rightsAttribution 2.0 Generic
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/
dc.titlet-tests, non-parametric tests, and large studies—a paradox of statistical practice?
dc.typeJournal article
dc.date.updated2015-10-20T10:55:31Z
dc.creator.authorFagerland, Morten W
dc.identifier.doihttp://dx.doi.org/10.1186/1471-2288-12-78
dc.identifier.urnURN:NBN:no-51286
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/47146/1/12874_2012_Article_774.pdf
dc.type.versionPublishedVersion
cristin.articleid78


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