Audit, revise, or draft manuscript statistical reporting for Nature / high-impact journal submissions. Use when the user asks to check statistical analysis sections, p values, confidence intervals, sample size, biological versus technical replicates, randomization, blinding, multiple-comparison corr

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. Output format Unless the user asks for another format, return: For a clean drafting request with enough information, skip the long issue list and return: Red lines Do not invent p values, sample sizes, degrees of freedom, confidence intervals, software versions, correction methods, preregistration, exclusion rules, or power calculations. Do not recommend a statistical test as final when the unit of analysis or design is unclear. Do not accept n = number of cells/images/measurements as independent replication without checking the experimental hierarchy. Do not use “significant” as a synonym for important, large, causal, or biologically meaningful. Do not hide non significant or weak results by rewriting them into stronger claims. Do not give medical, regulatory, or clinical trial statistical advice beyond reporting checks unless the user provides the relevant protocol and asks for bounded manuscript wording. Related files File Open when [references/source basis.md](references/source basis.md) You need the source hierarchy or want to justify why the skill emphasizes transparency, reproducibility, and design reporting [references/nature article requirements.md](references/nature article requirements.md) The target is the flagship journal Nature or the user requests its exact statistical submission checklist [../nature shared/journal formats/nature machine intelligence.md](../nature shared/journal formats/nature machine intelligence.md) The target is Nature Machine Intelligence or NMI specific legend, source data, reporting or stage requirements affect the audit [references/statistical reporting.md](references/statistical reporting.md) You are drafting or auditing Statistical analysis, Methods, Results, or Supplementary Methods text [references/common failure modes.md](references/common failure modes.md) You see nested measurements, many comparisons, interaction claims, correlation/regression, outliers, tiny samples, or overstrong p value language [references/figure statistics.md](references/figure statistics.md) You are checking figure legends, panel statistics, error bars, stars, box/violin plots, source data notes, or graphical reporting [references/reviewer checklist.md](references/reviewer checklist.md) You are finalizing an audit or preparing a reviewer facing risk summary [../nature shared/core/consistency sweep.md](../nature shared/core/consistency sweep.md) The same statistic appears in more than one place, or interval terminology is in question: one metric at two precisions across table and text, SD/Std abbreviation drift, confidence interval used where prediction interval is meant, or overlapping error bars described as outperformance Source hierarchy Use sources in this order: 1. User supplied manuscript, data, protocol, statistical analysis plan, reviewer comments, and journal instructions. 2. Nature Portfolio reporting standards and reporting summary requirements. 3. Nature Methods / Nature Portfolio statistics guidance summarized in references/source basis.md . 4. Study type reporting guidelines where relevant, for example CONSORT, STROBE, PRISMA, ARRIVE, or field specific community standards. 5. Conservative statistical reporting practice. If the supplied material is insufficient for a defensible statistical recommendation, ask for the missing design facts or provide a bounded wording option rather than guessing.

nature-statistics