Statcheck in Canada: What proportion of CPA journal articles contain errors in the reporting of p-values?
Bibliographic record
Abstract
Using a computer program called "Statcheck," a 2016 digital survey of several prestigious American and European psychology journals showed that the p-values reported in research articles failed to agree with the corresponding test statistics (e.g., F, t, χ 2 ) at surprisingly high rates: nearly half of all articles contained at least one such error, as did about 10% of all null hypothesis significance tests.We investigated whether this problem was present in Canadian psychology journals and, if so, at what frequency.We discovered similar rates of p-value errors in Canadian journals over the past 30 years.However, we also noticed, a large number of typographical errors in the electronic versions of the articles.When we hand corrected a sample of our articles, the per-article error rate remained about the same, but the per test rate of errors dropped to 6.3%.We recommend that, in future, journals include explicit checks of statistics in their editorial processes.
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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 | 0.293 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.050 | 0.065 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".