The Precision of Effect Size Estimation From Published Psychological Research
Why this work is in the frame
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Bibliographic record
Abstract
Confidence interval ( CI) widths were calculated for reported Cohen's d standardized effect sizes and examined in two automated surveys of published psychological literature. The first survey reviewed 1,902 articles from Psychological Science. The second survey reviewed a total of 5,169 articles from across the following four APA journals: Journal of Abnormal Psychology, Journal of Applied Psychology, Journal of Experimental Psychology: Human Perception and Performance, and Developmental Psychology. The median CI width for d was greater than 1 in both surveys. Hence, CI widths were, as Cohen (1994) speculated, embarrassingly large. Additional exploratory analyses revealed that CI widths varied across psychological research areas and that CI widths were not discernably decreasing over time. The theoretical implications of these findings are discussed along with ways of reducing the CI widths and thus improving precision of effect size estimation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.295 | 0.575 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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 it