Skill detail
nature-statistics
Audits and improves manuscript statistical reporting, covering experimental units, replication, uncertainty, tests, and figure statistics, with conservative ready-to-paste text.
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SKILL.md
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--- name: nature-statistics description: "Audit or improve manuscript statistical reporting, including experimental units, replication, uncertainty, tests, and figure statistics. Use for 统计审查、统计方法小节、图注统计 and reviewer concerns; compute new analyses only when requested with data." --- # Nature Statistics Reporting Skill Use this skill to make manuscript statistics transparent, reproducible, and appropriately bounded. It is a reporting and review skill, not a substitute for a statistician reanalysing raw data unless the user supplies the data and explicitly asks for computation. ## Default stance - Prioritize design transparency over decorative statistical language. - Separate three questions: what was measured, what unit was analysed, and what inference was claimed. - Treat the independent experimental unit as the default `n`; do not silently treat cells, fields of view, repeated readings, spectra, model runs, or technical replicates as independent biological or experimental samples. - Prefer effect sizes, uncertainty intervals, sample sizes, and exact test definitions over significance-only phrasing. - State missing information as `AUTHOR_INPUT_NEEDED` instead of inventing sample sizes, tests, software, corrections, exclusion rules, randomization, or blinding. - If a journal-specific instruction, study-type guideline, or field standard conflicts with this skill, follow the more specific source and mark the source used. ## Accepted inputs The skill may receive: - a Statistical analysis / Methods subsection - Results paragraphs containing test statistics or p values - figure panels, legends, captions, or source-data notes - reviewer comments about statistics - author notes in Chinese or English - tables of reported comparisons - raw or summary data, only when the user wants a concrete reanalysis or figure-statistics check If the input is partial, run a bounded audit and state which parts cannot be assessed. ## Workflow 1. **Classify the task.** Decide whether the user wants audit, rewrite, draft, reviewer-response support, figure-statistics alignment, or data-backed reanalysis. 2. **Extract the design.** Identify groups, treatments, time points, endpoints, blocking factors, repeated measures, randomization, blinding, exclusions, and missing-data handling. 3. **Define `n` and replication.** Separate independent experimental units, biological replicates, technical replicates, repeated measures, cells/fields/subsamples, simulations, and pooled observations. 4. **Map claims to analyses.** For each result claim, record the comparison/model, test family, assumptions, correction strategy, effect estimate, uncertainty, and exact p-value policy. 5. **Check common failure modes.** Use `references/common-failure-modes.md` when the text involves nested data, many comparisons, cell-level measurements, interaction claims, correlations, regression, outliers, small samples, or significance-only reasoning. 6. **Check reporting completeness.** Use `references/statistical-reporting.md` to verify that Methods and Results give enough information for readers and reviewers to understand the analysis. If the target is the flagship journal Nature, also use `references/nature-article-requirements.md` for its exact tail, `n`, repeat, P-value, test-statistic and degrees-of-freedom requirements. If the target is Nature Machine Intelligence, also use `../nature-shared/journal-formats/nature-machine-intelligence.md` for its legend-statistics, source-data, reporting-summary and stage-specific checks. 7. **Align figure statistics.** Use `references/figure-statistics.md` when figure legends, panel labels, stars, error bars, box plots, violin plots, source data, or supplementary figure notes are involved. 8. **Draft or revise.** Produce conservative, ready-to-paste text. Keep claims within the supplied design and evidence. Do not upgrade statistical association into mechanism or causality. 9. **Run final QA.** Use `references/reviewer-cRead the full source on GitHub (opens external page)