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dc.contributor.authorAbt, Grant
dc.contributor.authorBoreham, Colin
dc.contributor.authorDavison, Gareth
dc.contributor.authorJackson, Robin
dc.contributor.authorNevill, Alan
dc.contributor.authorWallace, Eric
dc.contributor.authorWilliams, Mark
dc.date.accessioned2020-07-31T12:42:36Z
dc.date.available2020-07-31T12:42:36Z
dc.date.issued2020-06-19
dc.identifier.citationAbt, G., Boreham, C., Davison, G., Jackson, R. et al. (2020) Power, precision, and sample size estimation in sport and exercise science research, Journal of Sports Sciences. DOI: 10.1080/02640414.2020.1776002en
dc.identifier.issn0264-0414en
dc.identifier.pmid32558628 (pubmed)
dc.identifier.doi10.1080/02640414.2020.1776002en
dc.identifier.urihttp://hdl.handle.net/2436/623402
dc.descriptionThis is an accepted manuscript of an article published by Taylor and Francis in Journal of Sports Sciences on 19/06/2020, available online: https://doi.org/10.1080/02640414.2020.1776002 The accepted version of the publication may differ from the final published version.en
dc.description.abstractThe majority of papers submitted to the Journal of Sports Sciences are experimental. The data are collected from a sample of the population and then used to test hypotheses and/or make inferences about that population. A common question in experimental research is therefore “how large should my sample be?”. Broadly, there are two approaches to estimating sample size – using power and using precision. If a study uses frequentist hypothesis testing, it is common to conduct a power calculation to determine how many participants would be required to reject the null hypothesis assuming an effect of a given size is present. That is, if there’s an effect of the treatment (of given size x), a power calculation will determine approximately how many participants would be required to detect that effect (of size x or larger) a given percentage of the time (often 80%). Power calculations as conducted in popular software programmes such as G*Power (Faul et al., 2009) typically require inputs for the estimated effect size, alpha, power (1 – ᵦ), and the statistical tests to be conducted. All of these inputs are subjective (or informed by previous studies) and up to the researcher to decide the most appropriate balance between type 1 error rate (false positive), type 2 error rate (false negative), cost, and time. In contrast, estimating sample size via precision involves estimating how many participants would be required for the frequentist confidence interval or Bayesian credible interval resulting from a statistical analysis to be of a certain width. The implication is that a narrower confidence interval or credible interval allows a more precise estimation of where the “true” population parameter (e.g., mean difference) might be.en
dc.formatapplication/pdfen
dc.languageeng
dc.language.isoenen
dc.publisherInforma UK Limiteden
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/02640414.2020.1776002en
dc.titlePower, precision, and sample size estimation in sport and exercise science researchen
dc.typeJournal articleen
dc.identifier.eissn1466-447X
dc.identifier.journalJournal of Sports Sciencesen
dc.date.updated2020-07-09T15:18:50Z
dc.contributor.institutionSports Performance.
pubs.place-of-publicationEngland
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW31072020ANen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2021-06-19en
dc.source.beginpage1
dc.source.endpage3
dc.description.versionPublished version
refterms.dateFCD2020-07-31T12:40:53Z
refterms.versionFCDAM


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