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dc.contributor.authorLandler, Lukas
dc.contributor.authorRuxton, Graeme D.
dc.contributor.authorMalkemper, E. Pascal
dc.identifier.citationLandler , L , Ruxton , G D & Malkemper , E P 2020 , ' Grouped circular data in biology : advice for effectively implementing statistical procedures ' , Behavioral Ecology and Sociobiology , vol. 74 , no. 8 , 100 .
dc.identifier.otherPURE: 269363921
dc.identifier.otherPURE UUID: e993fe94-287d-4ff6-a775-2aaaf977fc99
dc.identifier.otherScopus: 85088256830
dc.identifier.otherORCID: /0000-0001-8943-6609/work/78205023
dc.identifier.otherWOS: 000550233400001
dc.descriptionOpen access funding provided by Austrian Science Fund (FWF). LL was partially funded by the Austrian Science Fund (FWF, Grant Number: P32586).en
dc.description.abstractThe most common statistical procedure with a sample of circular data is to test the null hypothesis that points are spread uniformly around the circle without a preferred direction. An array of tests for this has been developed. However, these tests were designed for continuously distributed data, whereas often (e.g. due to limited precision of measurement techniques) collected data is aggregated into a set of discrete values (e.g. rounded to the nearest degree). This disparity can cause an uncontrolled increase in type I error rate, an effect that is particularly problematic for tests that are based on the distribution of arc lengths between adjacent points (such as the Rao spacing test). Here, we demonstrate that an easy-to-apply modification can correct this problem, and we recommend this modification when using any test, other than the Rayleigh test, of circular uniformity on aggregated data. We provide R functions for this modification for several commonly used tests. In addition, we tested the power of a recently proposed test, the Gini test. However, we concluded that it lacks sufficient increase in power to replace any of the tests already in common use. In conclusion, using any of the standard circular tests (except the Rayleigh test) without modifications on rounded/aggregated data, especially with larger sample sizes, will increase the proportion of false-positive results—but we demonstrate that a simple and general modification avoids this problem.
dc.relation.ispartofBehavioral Ecology and Sociobiologyen
dc.rightsCopyright © The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
dc.subjectGini testen
dc.subjectHermans-Rasson testen
dc.subjectRao’s spacing testen
dc.subjectRayleigh testen
dc.subjectRounding erroren
dc.subjectType I erroren
dc.subjectQH301 Biologyen
dc.subjectEcology, Evolution, Behavior and Systematicsen
dc.subjectAnimal Science and Zoologyen
dc.titleGrouped circular data in biology : advice for effectively implementing statistical proceduresen
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.description.statusPeer revieweden

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