The inconsistency of p-curve: Testing its reliability using the power pose and HPA debates
Bibliographic record
Abstract
Recent works have called into question whether p-curve can reliably assess the presence of "evidential value" within a set of studies. To examine an as-yet unexplored issue, we examined the method used to identify p-values for inclusion in a p-curve analysis. We developed iterated p-curve analysis (IPA), which calculates and p-curves every permutation for a set of reported p-values, and applied it to the data reported in several published p-curve analyses. Specifically, we investigated two phenomena for which p-curves have been used to evaluate the presence of evidential value: the power pose and the hypothalamic-pituitary-adrenal (HPA) reactivity debates. The iterated p-curve analyses revealed that the p-curve method fails to provide reliable estimates or reproducible conclusions. We conclude that p-curve should not be used to make conclusions regarding the presence or absence of evidence for a specific phenomenon.
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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 | 0.558 | 0.868 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".