Effect sizes for equivalence testing: Incorporating the equivalence interval
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
Equivalence testing (ET) is a framework for determining if an effect is small enough to be considered meaningless, wherein meaningless is expressed as an equivalence interval (EI). Although traditional effect sizes (ESs) are important accompaniments to ET, these measures exclude information about the EI. Incorporating the EI is valuable for quantifying how far the effect is from the EI bounds. The proportional distance (PD) from an observed effect to the smallest effect that would render it meaningful is proposed as an ES measure for ET. We conducted two Monte Carlo simulations to evaluate the PD when applied to (1) mean differences and (2) correlations. The coverage rate and bias of the PD were excellent within the investigated conditions. We also applied the PD to two recent psychological studies. These applied examples revealed the beneficial properties of the PD, namely its ability to supply information above and beyond other statistical tests and ESs.
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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 | 0.060 | 0.569 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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