A more powerful familywise error control procedure for evaluating mean equivalence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When one wishes to show no meaningful differences among group means, equivalence tests should be used, as a nonsignificant test of mean difference does not provide evidence supporting equivalence. This research proposes two modified stepwise procedures for controlling the familywise Type I error rate, based on the Bonferroni-type correction of k2/4 (where k is the number of groups to be compared) proposed by Caffo, Lauzon and Rohmel (2013 Correction to “easy multiplicity control in equivalence testing using two One-Sided tests. The American Statistician 67 (2):115–6) Bonferroni-type correction of k2/4 (where k is the number of groups to be compared). Using a Monte Carlo simulation method, we show that adopting a stepwise procedure increases power, while maintaining the familywise error rate at or below α. Implications for applied research and directions for future study are discussed.
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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 | 0.003 | 0.026 |
| 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) | 0.000 | 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