Statistical reporting deficiencies in environmental toxicology
Bibliographic record
Abstract
Null hypothesis significance testing is one of the most widely used forms of statistical testing in environmental toxicology. In this short communication, the authors show that the reporting of statistical information when using null hypothesis significance testing is frequently inadequate in environmental toxicology research. The authors demonstrate this by analyzing the statistical information reported for papers employing t tests or analyses of variance in the Environmental Toxicology section of Environmental Toxicology and Chemistry in 2010, which comprised 68% of papers published by this journal in that year. Of these papers, 60% fail to report exact p values, 85% fail to provide degrees of freedom, and 90% fail to report critical effect sizes. Statistical power was reported in only <2% of the published papers. The insufficient provision of statistical information makes interpretation of study results by reviewers and readers difficult. Consistently reporting exact p values with degrees of freedom, considering and explicitly stating biologically relevant critical effect sizes, and reporting statistical power associated with nonsignificant results would be easy to implement and would promote scientific progress in environmental toxicology through increased statistical transparency.
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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 | 0.387 | 0.834 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".