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Seven sins when interpreting statistics in sports injury science
  1. Rasmus Oestergaard Nielsen1,
  2. Cassandra M Chapman2,
  3. Winnifred R Louis2,
  4. Steven D Stovitz3,
  5. Mohammad Ali Mansournia4,5,
  6. Johann Windt6,
  7. Merete Møller7,
  8. Erik Thorlund Parner1,
  9. Adam Hulme8,
  10. Michael Lejbach Bertelsen1,
  11. Caroline F Finch9,
  12. Marti Casals10,11,
  13. Evert Verhagen12
  1. 1Department of Public Health, Aarhus University, Aarhus, Denmark
  2. 2School of Psychology, University of Queensland, Brisbane, Queensland, Australia
  3. 3Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
  4. 4Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  5. 5Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
  6. 6Experimental Medicine Program, University of British Columbia, Vancouver, British Columbia, Canada
  7. 7Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
  8. 8Centre for Human Factors and Sociotechnical Systems, University of the Sunshine Coast, Queensland, Australia
  9. 9Australian Centre for Research into Injury in Sport and its Prevention, Federation University Australia, Ballarat, Australia
  10. 10Sport Performance Analysis Research Group, University of Vic, Barcelona, Spain
  11. 11Research Centre Network for Epidemiology and Public Health (CIBERESP), Barcelona, Spain
  12. 12Amsterdam Collaboration on Health and Safety in Sports, Public and Occupational Health, VU University Medical Center, Amsterdam, The Netherlands
  1. Correspondence to Professor Mohammad Ali Mansournia, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; mansournia_ma{at}yahoo.com

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Introduction

The British Journal of Sports Medicine has introduced a series of editorials and infographics that explain the value of using appropriate methodology in sports injury research.1–6 Indeed, proper methodology is necessary for understanding why sports injuries develop, how best to prevent them and which therapeutic interventions will be most effective. Without correctly applying and interpreting statistics, subjective intuitions could lead to incorrect conclusions. In this editorial, we present seven common ‘statistical sins’ made in research and discuss how to present research findings in such a way as to help athletes, coaches and clinicians avoid drawing flawed conclusions when attempting to interpret causality in sports injury research. The sins have been adapted from an article originally published in The Conversation.7

Sin #1: trusting coincidence

Did you know that NFL teams with an animal team logo (eg, Denver Broncos and Carolina Panthers) have a dramatic 15% reduced risk of concussions compared with NFL teams without animal logos (eg, Tennessee Titans and Pittsburgh Steelers)?8 If one looks hard enough, apparently interesting associations and spurious correlations between phenomena can be found almost everywhere. However, simply because two things happen to change in parallel, or follow a similar pattern, does not mean they are causally related.

To avoid this sin, one must thoughtfully consider whether the association is likely to be causal or non-causal. Most sports injury studies simply examine non-causal associations, which are sometimes wrongly interpreted as being causal. Think of concussions and animal logos next time you read a sports injury article: are the reported relationships likely to be causally …

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