Statistical reporting practices within forensic psychology: Is anything changing?
Bibliographic record
Abstract
OBJECTIVE: We examined the evolution of statistical reporting practices within forensic psychology across two decades (2000-2020) to assess their adherence to recommended best practices. METHOD: = 813). We then evaluated the use and interpretation of null hypothesis significance testing (NHST), effect sizes (ESs), confidence intervals (CIs), and Bayesian statistics for each article in the sample. RESULTS: We found a persistent reliance on NHST, with nearly all articles employing it for data analysis and interpretation. Encouragingly, the reporting of ESs and CIs has increased substantially; their interpretative use, however, remains limited. Bayesian methods were rarely used for analysis or interpretation of data. CONCLUSIONS: -hacking). We call for journals in the field to encourage further use of statistical best practices within their manuscripts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.421 | 0.773 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".