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Record W2118262583 · doi:10.1002/jclp.10217

Recommendations for applying tests of equivalence

2003· article· en· W2118262583 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Clinical Psychology · 2003
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsUniversity of WindsorYork University
Fundersnot available
KeywordsEquivalence (formal languages)PsychologyStatisticsTest (biology)Null hypothesisPopulationMathematicsSocial psychologyEconometricsDiscrete mathematicsMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.451
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.430
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.451
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.917
GPT teacher head0.754
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it