Tests de randomisation : une façon plus flexible de tester la significativité avec le logiciel Stata
Bibliographic record
Abstract
En sciences régionales, plusieurs conclusions empiriques reposent sur des tests de significativité. La significativité des paramètres dans des modèles de régression revêt souvent une importance capitale pour les chercheurs. Or, le calcul des tests statistiques classiques (test-t ou test-F) repose sur un certain nombre d’hypothèses qui, si elles s’avèrent non-respectées, peuvent entraîner un biais dans les calculs des statistiques. Le but de cette note technique est de présenter une approche alternative, un test non-paramétrique, permettant de tester la significativité à partir d’un test de randomisation. Cette approche est plus flexible que les tests paramétriques conventionnels et son application est relativement simple. Afin de démontrer le potentiel de la méthode, une application détaillée dans le logiciel Stata est présentée sur la base de données fictives.
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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 | 0.136 | 0.445 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".