| name | sci-statistics |
| description | Use to enforce Science's statistics and reproducibility reporting — n and replication, test choice and assumptions, effect sizes with uncertainty, multiple-comparison control, randomization/blinding, and pre-registration where relevant. |
Statistics & Reproducibility (sci-statistics)
When to trigger
- Results report P values but not effect sizes or n.
- "Three independent experiments" is claimed but replication is unclear.
- Multiple comparisons are run with no correction.
- A reviewer is likely to ask "were analyses pre-specified?" and there's no answer.
The reporting backbone (every quantitative claim)
Each claim needs: effect size + uncertainty + n + test + what n means.
Replication and design
- Distinguish biological replication (independent samples) from technical replication (re-measurement). Science cares about the former.
- State how the sample size was chosen (power analysis or rationale), not post-hoc.
- Report randomization of subjects/treatments and blinding of measurement/analysis where applicable, or state why not.
- Report inclusion/exclusion criteria and any data excluded, with reasons, decided in advance.
Avoid the classic reviewer kills
- Pseudoreplication: treating technical replicates / cells from one animal as independent n.
- HARKing / p-hacking: presenting exploratory findings as confirmatory. If exploratory, label them.
- "Representative" images with no quantification across replicates.
- Bar chart + SEM masking a tiny, variable n.
- Comparing two effects by their significance ("significant here, not there") instead of testing the difference.
Reproducibility package
- Analysis code in a repository (see
sci-data), with a README and environment/versions.
- A reproducibility / reporting summary if requested by the journal — list software, versions, seeds.
- Deterministic where possible; report random seeds for simulations/ML.
Pre-registration & transparency (where relevant)
- For confirmatory studies (especially with human subjects/behavioral work), note pre-registration (OSF/AsPredicted) if done.
- Separate pre-specified analyses from post-hoc exploration explicitly in the text.
Statistics pass for Science
Use this as a second-pass capability check. First lock the broad discovery claim, decisive evidence, uncertainty/limitations, and why the result belongs in a general-science weekly; then test whether the manuscript addresses general-science reviewers and editors who ask whether the result changes a broad field, is technically decisive, and can be understood outside the subdiscipline.
- Primary move: Check estimand, denominator, uncertainty, multiplicity, missing data, sensitivity, and reporting standard before interpreting any result.
- Decision ledger: return
claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
- Neighbor test: compare against Nature for similar broad-scope novelty, PNAS for academy-wide breadth, specialist journals when the claim is field-internal; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
- Verification floor: before submission-ready advice, re-open
resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.
Output format
【Per-claim backbone】 effect+CI / n / unit-of-n / test / assumptions → list gaps
【Replication】 biological vs technical clear? yes/no
【Sample-size rationale】 power/justification present? yes/no
【Randomization & blinding】 reported / N/A-justified / missing
【Multiplicity】 corrected? method
【Reproducibility】 code + versions + seeds present? yes/no
【Next】 sci-data
Anti-patterns
- Do not report P without effect size and n.
- Do not count technical replicates as independent observations.
- Do not infer "no effect" from a non-significant test on an underpowered sample.
- Do not present post-hoc subgroup findings as if pre-specified.
