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Common scientific and statistical errors in obesity research
Author(s) -
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.
Publication year - 2016
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1002/oby.21449
Subject(s) - statistician , statistical hypothesis testing , nonparametric statistics , statistical significance , statistics , p value , quality (philosophy) , regression toward the mean , computer science , medline , data science , medicine , mathematics , political science , law , philosophy , epistemology
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.