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Obesity (Silver Spring). 2016 Apr;24(4):781-90. doi: 10.1002/oby.21449.

Common scientific and statistical errors in obesity research.

Author information

1
Office of Energetics, University of Alabama at Birmingham, Birmingham, Alabama, USA.
2
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA.
3
Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, Alabama, USA.
4
Department of Nutritional Sciences, Texas Tech University, Lubbock, Texas, USA.
5
Department of Biostatistics, University at Buffalo, Buffalo, New York, USA.
6
Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
7
Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
8
Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
9
Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, Alabama, USA.
10
Department of Epidemiology & Community Health, University of Minnesota, Minneapolis, Minnesota, USA.
11
Department of Health Policy and Management, University of North Carolina, Chapel Hill, North Carolina, USA.
12
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

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.

PMID:
27028280
PMCID:
PMC4817356
DOI:
10.1002/oby.21449
[Indexed for MEDLINE]
Free PMC Article

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