Common scientific and statistical errors in obesity research.


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


  • eng

Conference Location

  • United States