| name | nature-statistics |
| description | Statistical reporting and audit for scientific manuscripts, figures, and supplementary materials. Use for p-values, sample size, replicates, effect sizes, confidence intervals, multiple comparisons, model assumptions, or cross-section numerical consistency. |
Nature Statistics
Audit the analysis and the reporting separately.
- Identify the experimental unit, observational unit, sample size, biological and technical replicates, exclusions, missing values, and randomization or blocking.
- Check that the statistical test matches the design, outcome type, dependence structure, distributional assumptions, and comparison question.
- Report effect size and uncertainty alongside p-values. State the multiplicity procedure when multiple hypotheses or time points are tested.
- Trace every number across text, tables, figures, captions, supplement, and abstract. Flag inconsistent denominators, rounding, units, degrees of freedom, and significant-figure precision.
- Distinguish exploratory, confirmatory, model-based, and descriptive analyses. Do not repair a flawed analysis by silently changing the method.
Return a finding table with location, issue, severity, evidence, recommended wording, and whether reanalysis is required. Do not invent data, test results, or a compliant journal policy; verify venue-specific rules when the target journal is known.
This is a ScanSci adaptation of the nature-statistics workflow from Yuan1z0825/nature-skills. R workflows should use the repository-configured Rscript path when execution is explicitly requested.
