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dc.contributor.authorHumphreys, Rosalind K.
dc.contributor.authorPuth, Marie-Therese
dc.contributor.authorNeuhäuser, Markus
dc.contributor.authorRuxton, Graeme Douglas
dc.date.accessioned2018-08-06T09:30:08Z
dc.date.available2018-08-06T09:30:08Z
dc.date.issued2019-01
dc.identifier.citationHumphreys , R K , Puth , M-T , Neuhäuser , M & Ruxton , G D 2019 , ' Underestimation of Pearson's product moment correlation statistic ' , Oecologia , vol. 189 , no. 1 , pp. 1-7 . https://doi.org/10.1007/s00442-018-4233-0en
dc.identifier.issn0029-8549
dc.identifier.otherPURE: 255043566
dc.identifier.otherPURE UUID: fc1de5a2-b9ba-4293-a22c-9041a15814ca
dc.identifier.otherScopus: 85051081183
dc.identifier.otherORCID: /0000-0001-7266-7523/work/48774960
dc.identifier.otherORCID: /0000-0001-8943-6609/work/60427517
dc.identifier.otherWOS: 000455167600001
dc.identifier.urihttp://hdl.handle.net/10023/15770
dc.description.abstractPearson’s product moment correlation coefficient (more commonly Pearson’s r) tends to underestimate correlations that exist in the underlying population. This phenomenon is generally unappreciated in studies of ecology, although a range of corrections are suggested in the statistical literature. The use of Pearson’s r as the classical measure for correlation is widespread in ecology, where manipulative experiments are impractical across the large spatial scales concerned; it is therefore vital that ecologists are able to use this correlation measure as effectively as possible. Here, our literature review suggests that corrections for the issue of underestimation in Pearson’s r should not be adopted if either the data deviate from bivariate normality or sample size is greater than around 30. Through our simulations, we then aim to offer advice to researchers in ecology on situations where both distributions can be described as normal, but sample sizes are lower than around 30. We found that none of the methods currently offered in the literature to correct the underestimation bias offer consistently reliable performance, and so we do not recommend that they be implemented when making inferences about the behaviour of a population from a sample. We also suggest that, when considering the importance of the bias towards underestimation in Pearson’s product moment correlation coefficient for biological conclusions, the likely extent of the bias should be discussed. Unless sample size is very small, the issue of sample bias is unlikely to call for substantial modification of study conclusions.
dc.format.extent7
dc.language.isoeng
dc.relation.ispartofOecologiaen
dc.rights© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectAssociationen
dc.subjectBiasen
dc.subjectCorrelationen
dc.subjectPearson’s ren
dc.subjectSamplingen
dc.subjectHA Statisticsen
dc.subjectQH301 Biologyen
dc.subjectT-NDASen
dc.subject.lccHAen
dc.subject.lccQH301en
dc.titleUnderestimation of Pearson's product moment correlation statisticen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1007/s00442-018-4233-0
dc.description.statusPeer revieweden
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00442-018-4233-0#SupplementaryMaterialen


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