Biological Statistics (cc-statistics)

When to trigger

• n is ambiguous, or you suspect pseudo-replication
• Unsure which test fits the data and design
• Many comparisons without multiplicity correction
• Error bars / variability are unlabeled in figures or legends

Defining n (the core issue)

• n = number of independent biological replicates (separate mice, independent cultures/passages, distinct patients).
• Technical replicates (duplicate wells, repeat reads) describe measurement precision and do not count toward n.
• State n for every panel in the legend, with what one unit is ("n = 5 mice per group", "n = 3 independent experiments").
• Pooling cells from many wells of one experiment and calling it n=many is pseudo-replication — a classic Cancer Cell reviewer catch.

Choosing the test

Check assumptions (normality, equal variance) and report how. Prefer non-parametric or Welch corrections for small/uneven bench-scale data.

Multiple comparisons

• Few planned comparisons → Tukey / Dunnett / Holm / Bonferroni.
• Genome-wide / omics → control the false discovery rate (Benjamini-Hochberg); report adjusted p / q-values.
• Do not run many pairwise t-tests across groups or timepoints without correction.

Error bars and reporting

• Define what every error bar is: SD, SEM, or 95% CI — in the legend.
• Show data points (dot plots / superplots) rather than bar-only charts when n is small.
• Report exact p-values (not just asterisks) where feasible, plus the test and n.
• Distinguish biological-replicate variability from technical noise in plots.
• For survival, give hazard ratios with CIs, not p-value alone.

Checklist

• n defined per panel as biological replicates; one unit specified
• No pseudo-replication (technical reps not counted as n)
• Test choice matches design; assumptions checked
• Repeated/longitudinal data analyzed with appropriate model, not serial t-tests
• Multiple comparisons corrected; FDR for omics
• Error bars defined (SD/SEM/CI) in every legend
• Exact p-values, test name, and n reported
• Data points shown for small-n comparisons
• Statistical software + versions stated (in STAR Methods)

Anti-patterns

• "n=3" meaning three technical wells of one experiment
• SEM used to make tiny error bars without saying so
• Bar charts hiding 2–3 underlying points
• Multiple t-tests across timepoints/groups, uncorrected
• Asterisks with no test, no n, no exact p
• Treating omics features as independent without FDR control

Statistics pass for Cancer Cell

Use this as a second-pass capability check. First lock the cancer context, mechanism, model system, validation chain, and translational boundary; then test whether the manuscript addresses cancer-biology reviewers who expect mechanistic oncology, translational relevance, and strong multi-modal validation.

• 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 Cell for broader biology, Nature Cancer for oncology breadth, Clinical Cancer Research for clinical translation; 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

【n definition】biological unit = ...; per-panel n stated? Y/N
【Pseudo-replication risk】none / fix: [...]
【Tests】per analysis: ...
【Multiplicity】correction used: ...
【Error bars】SD/SEM/CI defined in legends? Y/N
【Reporting gaps】exact p / data points / software version
【Next step】cc-figures-tables