Common scientific and statistical errors in obesity research

Brandon J. George, T. Mark Beasley, Andrew W. Brown, John Dawson, Rositsa Dimova, Jasmin Divers, Tashauna U. Goldsby, Moonseong Heo, Kathryn A. Kaiser, Scott W. Keith, Mimi Y. Kim, Peng Li, Tapan Mehta, J. Michael Oakes, Asheley Skinner, Elizabeth Stuart, David B. Allison

Research output: Contribution to journalReview article

47 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)781-790
Number of pages10
JournalObesity
Volume24
Issue number4
DOIs
StatePublished - Apr 1 2016

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    George, B. J., Beasley, T. M., Brown, A. W., Dawson, J., Dimova, R., Divers, J., Goldsby, T. U., Heo, M., Kaiser, K. A., Keith, S. W., Kim, M. Y., Li, P., Mehta, T., Oakes, J. M., Skinner, A., Stuart, E., & Allison, D. B. (2016). Common scientific and statistical errors in obesity research. Obesity, 24(4), 781-790. https://doi.org/10.1002/oby.21449