Nature Statistics Reporting Skill

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

• 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

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.
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

Statistics review scope
- Input reviewed:
- Boundary / missing materials:
- Study design readout:
- Independent unit and replication readout:

Major statistical issues
- [P0/P1/P2] Issue:
  Evidence from supplied text:
  Why it matters:
  Fix:

Ready-to-paste revision
[Rewritten Statistical analysis / Results / figure legend text]

AUTHOR_INPUT_NEEDED
- [short factual questions only]

Reviewer-risk note
- What a statistical reviewer may still challenge:

Draft Statistical analysis
[ready-to-paste text]

Reporting notes
- n definition:
- tests/models:
- multiple comparisons:
- software/version:
- unresolved fields:

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

Source hierarchy

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.