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.
Bibliographical noteFunding Information:
Funded in part by NIH grants P30DK056336, R25HL124208, R25DK099080, R01MH099010, R01MD009055, R25GM116167, and T32DK062710. The opinions expressed are those of the authors and not necessarily the NIH or any other organization.
© 2016 The Obesity Society.
Copyright 2016 Elsevier B.V., All rights reserved.