Common scientific and statistical errors in obesity research.
Published
Journal Article (Review)
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.
Full Text
Duke Authors
Cited Authors
- George, BJ; Beasley, TM; Brown, AW; Dawson, J; Dimova, R; Divers, J; Goldsby, TU; Heo, M; Kaiser, KA; Keith, SW; Kim, MY; Li, P; Mehta, T; Oakes, JM; Skinner, A; Stuart, E; Allison, DB
Published Date
- April 2016
Published In
Volume / Issue
- 24 / 4
Start / End Page
- 781 - 790
PubMed ID
- 27028280
Pubmed Central ID
- 27028280
Electronic International Standard Serial Number (EISSN)
- 1930-739X
Digital Object Identifier (DOI)
- 10.1002/oby.21449
Language
- eng
Conference Location
- United States