Comprehensive Evaluation of Reference Values of Parametric and Non-Parametric Effect Size Methods for Two Independent Groups
Bibliographic record
Abstract
In the field of health and other sciences, effect size (ES) provides a scientific approach to the effectiveness of treatment or intervention. The p-value indicates whether the statistical difference depends on chance, while ES gives information about the effectiveness of the treatment or intervention, even if the difference is not significant. For this reason, ES has become a very popular measure in recent years. It depends on which ES will be used based on the distribution of data and the number of groups. In this study, parametric and non-parametric ES were evaluated for two independent groups. When the literature was examined, there were no studies aimed at evaluating the reference values of the parametric and non-parametric ES methods used for two independent groups. In this study, the reference values of parametric and non-parametric ES methods for two independent groups were re-evaluated by a simulation study. As a result, the very small reference value of parametric ES methods was determined differently from the literature. It has been seen that the reference values of non-parametric ES methods are valid in cases where the skewness is low, and new reference values have been proposed at the varying skewness level.
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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 | 0.171 | 0.512 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".