Concise, Simple, and Not Wrong: In Search of a Short-Hand Interpretation of Statistical Significance
Bibliographic record
Abstract
One challenge when communicating science to practitioners and the general public is accurately representing statistical results. In particular, describing the meaning of statistical significance to a non-scientific audience is especially difficult given the technical nature of a correct definition. Correct interpretations of statistical significance can be unintuitive, nuanced, and use unfamiliar technical language. As a result, when researchers are tasked with providing short and understandable interpretations of statistical significance it can be tempting to default to convenient but incorrect interpretations. In the current paper, we offer a concise, simple, and correct interpretation of statistical significance that is suitable for communications targeting a general audience.
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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 | 0.108 | 0.518 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".