A correction on the Bradley and Brand method of estimating effect sizes from published literature
Bibliographic record
Abstract
Kühberger, Scherndl, and Fritz commented on an attempt by Bradley and Brand to adjust sets of exaggerated effect sizes reported in literatures associated with underpowered Null Hypothesis Statistical Tests (NHST). Their comment highlighted two important issues: (a) the senior author, Bradley, made an error in presenting the correction formula, and (b) there is an inherent incompatibility between inferential statistics and accurate measurement. The proper formula is presented here with evidence that the formula is relatively accurate in appropriately estimating effect sizes that have been exaggerated through NHST. The term relatively accurate is used since power cannot be 100%, and thus any attempt to estimate a true effect size will be out by some relationship between the alpha level, power, and of course statistical variability of the estimates.
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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 | 0.122 | 0.608 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.033 |
| Insufficient payload (model declined to judge) | 0.013 | 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".