nature-statistics

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-checklist.md before final delivery for severity labels, unresolved author questions, and reviewer-facing risk.