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Picking the right tools for the job: opening up the statistical toolkit to build a compelling case in sport and exercise medicine research
  1. Johann Windt1,2,3,
  2. Rasmus Oestergaard Nielsen4,
  3. Bruno D Zumbo5
  1. 1Experimental Medicine Program, University of British Columbia, Vancouver, British Columbia, Canada
  2. 2Sports Medicine Department, United States Olympic Committee, Colorado Springs, Colorado, USA
  3. 3United States Coalition for the Prevention of Illness and Injury in Sport, Colorado Springs, Colorado, USA
  4. 4Section for Sports Science, Department of Public Health, Aarhus University, Aarhus, Denmark
  5. 5Measurement, Evaluation, and Research Methodology, University of British Columbia, Vancouver, British Columbia, Canada
  1. Correspondence to Johann Windt, Experimental Medicine Program, University of British Columbia, Vancouver, BC V5Z 1M9, Canada; johannwindt{at}gmail.com

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P values have been the subject of debate for decades. Many researchers tend to think—or at least describe—their study outcomes to be either true or false based solely on p values.1 While a recent British Journal of Sports Medicine editorial provided a primer for sports medicine researchers to correctly understand p values,2 we aim to extend this discussion by reminding researchers to adopt a thoughtful approach to the entire statistical analysis process, using the relevant tools to build their case.

The goal of the sports medicine researcher—a building analogy

Scientific reasoning can be viewed as building a case for the existence (or non-existence) of phenomena. For sports medicine researchers, their specific goals may include the case for or against a treatment’s effectiveness, the risk of injury associated with certain risk factors, or the ability to predict a certain outcome given a set of criteria.

As with any building process, a number of steps are required. In this analogy, the process should include the following: designing the blueprint (preregistering studies where possible and outlining planned analyses), laying a foundation of sound data collection (appropriate sampling strategy, blinding where possible, ensuring measurement validity/reliability), understanding the flooring and roofing of study power (effect size, sample size and others), and painting the internal/external validity of the study. The entire process takes …

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