Picking the right tools for the job: opening up the statistical toolkit to build a compelling case in sport and exercise medicine research
Bibliographic record
Abstract
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 …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.131 | 0.457 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.022 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".