The Earth is spherical (p < 0.05): alternative methods of statistical inference
Bibliographic record
Abstract
A literature review was conducted to understand the limitations of well-known statistical analysis techniques, particularly analysis of variance.The review is structured around six major points: (1) averaging across participants can be misleading; (2) strong predictions are preferable to weak predictions; (3) constructs and measures should be distinguished conceptually and empirically; (4) statistical signi® cance and practical signi® cance should be distinguished conceptually and empirically; (5) the null hypothesis is virtually never true; and (6) one experiment is always inconclusive.Based on these insights, a number of lesser-known and less-frequently used statistical analysis techniques were identi® ed to address the limitations of more traditional techniques.In addition, a number of methodological conclusions about the conduct of human factors research are presented.
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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 | 0.174 | 0.565 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.026 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 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".