Recommendations for applying tests of equivalence
Bibliographic record
Abstract
Researchers in psychology reliably select traditional null hypothesis significance tests (e.g., Student's t test), regardless of whether the research hypothesis relates to whether the group means are equivalent or whether the group means are different. Tests of equivalence, which have been popular in biopharmaceutical studies for years, have recently been introduced and recommended to researchers in psychology for demonstrating the equivalence of two group means. However, very few recommendations exist for applying tests of equivalence. A Monte Carlo study was used to compare the test of equivalence proposed by Schuirmann with the traditional Student t test for deciding if two group means are equivalent. It was found that Schuirmann's test of equivalence is more effective than Student's t test at detecting population mean equivalence with large sample sizes; however, Schuirmann's test of equivalence performs poorly relative to Student's t test with small sample sizes and/or inflated variances.
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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 | 0.232 | 0.748 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.014 | 0.005 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.033 | 0.028 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".