A Monte-Carlo Estimation of Effect Size Distortion Due to Significance Testing
Bibliographic record
Abstract
A Monte-Carlo study was done with true effect sizes in deviation units ranging from 0 to 2 and a variety of sample sizes. The purpose was to assess the amount of bias created by considering only effect sizes that passed a statistical cut-off criterion of alpha = .05. The deviation values obtained at the .05 level jointly determined by the set effect sizes and sample sizes are presented. This table is useful when summarizing sets of studies to judge whether published results reflect an accurate appraisal of an underlying effect or a distorted estimate expected because significant studies are published and nonsignificant results are not. The table shows that the magnitudes of error are substantial with small sample sizes and inherently small effect sizes. Thus, reviews based on published literature could be misleading and especially so if true effect sizes were close to zero. A researcher should be particularly cautious of small sample sizes showing large effect sizes when larger samples indicate diminishing smaller effects.
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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 | 0.210 | 0.581 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".