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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

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.

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 …