Diagnosing Estimate Distortion Due to Significance Testing in Literature on Detection of Deception
Bibliographic record
Abstract
Studies journals typically report or feature results significant by statistical test criterion. This is a bias that prevents obtaining precise estimates of the magnitude of any underlying effect. It is severe with small effect sizes and small numbers of measurements. To illustrate the problem and a diagnosis technique, results of published studies on the detection of deception are graphed. The literature contains large effect sizes affirming that deceptive responses in contrast to truthful responses are associated with more reactive Skin Resistance Responses. These effect sizes when graphed on the x-axis against n on the y-axis are distributed as funnel graphs. A subset of studies show support for predicted small to medium effects on different physiological measures, individual differences, and condition manipulations. These effect sizes graphed by sample ns follow negative correlations, suggesting that effect sizes from published values of t, F, and zeta are exaggerations.
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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 | 0.363 | 0.828 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".