Seven sins when interpreting statistics in sports injury science
Bibliographic record
Abstract
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 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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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 | 0.090 | 0.393 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.015 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".