Approximate Confidence Intervals for Effect Sizes
Why this work is in the frame
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Bibliographic record
Abstract
This article defines an approximate confidence interval for effect size in correlated (repeated measures) groups designs. The authors found that their method was much more accurate than the interval presented and acknowledged to be approximate by Bird. That is, the coverage probability over all the conditions investigated was very close to the theoretical .95 value. By contrast, Bird’s interval could have coverage probability that was substantially below .95. In addition, the authors’interval was less likely than Bird’s method to present an overly optimistic portrayal of the effect. They also examined the operating characteristics of the Bird interval for effect size in an independent groups design and found that, although it is fairly accurate in its approximation of coverage probability, the accuracy of the approximation does vary with the magnitude of the population effect size.
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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 | 0.007 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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