Common scientific and statistical errors in obesity research.
This review identifies 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) e...
| Publicado en: | Obesity (19307381) Vol. 24; no. 4; pp. 781 - 791 |
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| Autores principales: | , , , , , , , , , , , , , , , , |
| Formato: | equations & formulas review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=114120055&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 114120055 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19307381 GSO jtl: Obesity (19307381) issn: 19307381 maglogo: Y pubinfo: dt: Apr2016 vid: 24 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 114120055 114120055 NLM27028280 114120055 10.1002/oby.21449 NLM27028280 PMC4817356 [Available on 04/01/17] 114120055 ppf: 781 ppct: 10 formats: tig: atl: Common scientific and statistical errors in obesity research. aug: au: George, Brandon J. Beasley, T. Mark Brown, Andrew W. Dawson, John Dimova, Rositsa Divers, Jasmin Goldsby, TaShauna U. Heo, Moonseong Kaiser, Kathryn A. Keith, Scott W. Kim, Mimi Y. Li, Peng Mehta, Tapan Oakes, J. Michael Skinner, Asheley Stuart, Elizabeth Allison, David B. affil: Office of Energetics, University of Alabama at Birmingham, Birmingham Alabama, USA sug: subj: Data Analysis, Statistical Study Design Standards Research, Medical Standards Bias (Research) Obesity Funding Source ab: This review identifies 10 common errors and problems in the statistical analysis, design, interpretation, and reporting of obesity research and discuss how they can be avoided. The 10 topics are: 1) misinterpretation of statistical significance, 2) inappropriate testing against baseline values, 3) excessive and undisclosed multiple testing and "P-value hacking," 4) mishandling of clustering in cluster randomized trials, 5) misconceptions about nonparametric tests, 6) mishandling of missing data, 7) miscalculation of effect sizes, 8) ignoring regression to the mean, 9) ignoring confirmation bias, and 10) insufficient statistical reporting. It is hoped that discussion of these errors can improve the quality of obesity research by helping researchers to implement proper statistical practice and to know when to seek the help of a statistician. pubtype: Academic Journal doctype: equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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