Sweeping recommendations regarding effect size and sample size can miss important nuances: A comment on “A comprehensive review of reporting practices in psychological journals”
Bibliographic record
Abstract
Statistical significance tests done with low statistical power levels can result in reports of exaggerated effect sizes. Funnel graphs can show these exaggerations for a given area of research by revealing a negative correlation between a study’s sample size on the y -axis and the effect size obtain by the study on the x -axis. A recent paper by Fritz, Scherndl, and Kühberger (2013) recommended that effect sizes should not be reported when there is a negative correlation between sample size and effect size for a given research area. This recommendation fails to consider magnitudes of the negative correlations and thereby misses the opportunity to mitigate effect size exaggerations and approximate more correct effect size estimates. This comment explains both the incorrectness of the recommendation and the approach and calculations necessary to correct effect size 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 | 0.097 | 0.391 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.076 | 0.084 |
| Insufficient payload (model declined to judge) | 0.006 | 0.012 |
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".