pnas-statistics

Statistics & Reproducibility (pnas-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 analysis is not reproducible from the deposited code (pnas-data).

The reporting backbone (every quantitative claim)

Each claim needs: effect size + uncertainty + n + test + what n means.

• n stated, with the unit of replication (biological vs technical replicates; cells vs animals vs subjects vs experiments).
• Effect size with 95% CI (preferred) or SD/SEM clearly labeled — not P alone.
• Exact P values (e.g., P = 0.013), not "P < 0.05", unless extremely small.
• Test named and justified (assumptions checked: normality, variance homogeneity, independence).
• Multiple comparisons corrected (Bonferroni/Holm/FDR) when many tests are run.

Replication and design

• Distinguish biological replication (independent samples) from technical replication (re-measurement). The former is what counts.
• State how the sample size was chosen (power analysis or explicit 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 excluded data, with reasons, decided in advance.

Discipline-specific notes across PNAS divisions

PNAS spans Biological, Physical, and Social Sciences, so match the rigor conventions of your division:

• Biological: replication unit, ARRIVE-style animal reporting, antibody/reagent validation.
• Social/behavioral: pre-registration is increasingly expected; report power, sampling frame, and deviations from the plan.
• Physical/computational: report uncertainties, error propagation, and numerical reproducibility (seeds, solver settings).

Avoid the classic reviewer kills

• Pseudoreplication: treating technical replicates / cells from one animal as independent n.
• HARKing / p-hacking: presenting exploratory findings as confirmatory. Label exploratory work as such.
• "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 pnas-data), with a README and environment/versions.
• A reproducibility/reporting summary if requested; list software, versions, seeds.
• Deterministic where possible; report random seeds for simulations/ML.

Pre-registration & transparency (where relevant)

• For confirmatory studies (especially human-subjects / behavioral work in the Social Sciences division), note pre-registration (OSF/AsPredicted) if done.
• Separate pre-specified analyses from post-hoc exploration explicitly in the text.

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
【Division-specific rigor】 (Bio / Physical / Social) conventions met? yes/no
【Reproducibility】 code + versions + seeds present? yes/no
【Next】 pnas-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.