A Proposed Specification Check for <i>p</i>-Hacking
Bibliographic record
Abstract
We propose a specification check for p-hacking. More specifically, we advocate the reporting of t-curves and mu-curves--the t-statistics and estimated effect sizes derived from regressions using every possible combination of control variables from the researcher's set--and introduce a standardized and accessible implementation. Our specification check allows researchers, referees, and editors to visually inspect variation in effect sizes, significativity, and sensitivity to the inclusion of control variables. We provide a Stata command that implements the specification check. Given the growing interest in estimating causal effects, the potential applicability of this specification check to empirical studies is large.
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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 | 0.193 | 0.634 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".