stats-check

name	stats-check
description	Run the agent_docs/statistics.md checklist over a Results section — flag bare p-values, missing N/effect-size/CI, causal overclaim on observational data, multiple-comparison issues, and numbers that disagree across text/tables/abstract
user-invocable	true

Stats Check

Core Rule

Every statistical claim must report effect size + uncertainty (a CI), N, the named test, and whether its assumptions hold. "Significant" means statistically significant — never "large" or "important". Association is not causation: correlational/observational designs license "associated with / predicts", never "causes / drives / improves". And no p-hacking / HARKing / selective reporting — the analysis you report is the analysis you pre-specified, with exploratory results labeled exploratory. A bare p-value, a missing N, a causal verb on an ablation, or a number that disagrees between the abstract and Table 2 are the failures this skill exists to catch.

This is the deterministic, numbers-and-design half of verification, operationalizing agent_docs/statistics.md. It flags and proposes fixes — it does not change a reported quantity (a Protected Claim per CLAUDE.md); recomputing a number to reconcile text and table is fine, altering what was measured or which test was run is not.

When to Use

Invoke with /stats-check when:

• A Results section with statistics is "done" and you want it Reviewer-2-proof.
• A revision added or changed a number, test, or comparison.
• Before submission, on Results + the abstract + every table — the consistency check needs all three open at once.
• The prompt-router flagged the task [Statistics] or [Causation].

Scope it: /stats-check sections/results.tex. Run the whole Results section with its tables and the abstract in view — half the findings are cross-location number mismatches.

Process

Phase 1: Load the Checklist and the Numbers

1. Read agent_docs/statistics.md — the reporting checklist this skill runs. It is the source of truth; this skill is its executor.
2. Read the target Results .tex, plus every table/figure it summarizes and the abstract. Numbers must agree across all three (statistics.md → text ↔ tables ↔ abstract); you cannot check consistency with only one open.
3. Read MANUSCRIPT_MAP.md — the Data & reproducibility line (was the design an RCT, an ablation, an observational comparison? — this decides what causal language is licensed) and Claims that need extra care.
4. For ML/agents work, read agent_docs/field/ai-ml.md — variance over seeds/rollouts, matched-budget comparison, and partial-vs-full success are field-specific statistics expectations.

If a number's test or assumptions are not stated and you cannot tell which was run, that is a finding — do not infer the test. Which test was run is a Protected Claim; ask the author.

Phase 2: Find Every Statistical Claim

Walk the section and extract each quantitative comparison or inferential statistic: every p-value, CI, effect size, mean difference, correlation, regression coefficient, percentage, ratio, and "significant"/"more"/"better" comparison. Record for each: the point estimate, the uncertainty (CI/SE/SD, or none), the N, the named test (or none), the verb (causes / is associated with / predicts), and the scope (which harness, which task set, all vs subset).

Phase 3: Check Each Claim Against the Checklist

Run every claim through agent_docs/statistics.md:

1. Effect size + uncertainty, not just p. A bare "p < 0.05" with no magnitude and no CI is incomplete — the reportable triplet is point estimate · interval · test (with N).
2. "Significant" used correctly. Flag "significant" deployed as a synonym for large / important / meaningful. Magnitude needs a number, stated separately.
3. N, test, assumptions stated. N for that comparison (not the study total); the test named; assumptions addressed (normality/variance for a t-test; linearity for Pearson r) and what was done if violated.
4. Multiple comparisons. A family of tests needs a correction (Bonferroni/Holm/BH-FDR) or a justification; all tests run must be disclosed, not just the winners. A subgroup "significant" after many slices is a hypothesis, not a finding.
5. Causation vs association. Check the verb against the design (Phase 1). A causal verb ("causes / drives / improves / the effect of") on a correlational comparison or an uncontrolled ablation is OVERCLAIM. RCT licenses causation within its population; a natural experiment does conditionally with the identifying assumption stated.
6. p-hacking / HARKing signals. Outcomes in Methods that vanish in Results (or appear in Results unannounced); p-values clustered just under 0.05; "trending toward significance"; post-hoc results framed as a-priori hypotheses; undisclosed exclusions or optional stopping.
7. Significant digits & units. False precision ("94.732%" off a ±1% estimate); the last digit should be the first uncertain one; units present and consistent; percentage points ≠ percent (a 70→88 rise is 18 pp, ~26% relative).
8. Cross-location consistency. Recompute: every number matches across text, tables, figures, and abstract; N is the same everywhere; totals add up; derived numbers (a difference, ratio, percent change) recompute from the reported inputs.

Phase 4: Report Findings and Fixes

Produce the findings table (claim → issue → fix), severity-ordered. The fix says what the author must do. Do not edit reported quantities here — flag the mismatch, propose the calibration. Reconciling a text number to a table (a clerical correction) can be proposed; changing what was measured or which test was run is a Protected Claim needing sign-off.

Output Format

# Stats Check — sections/results.tex

## Summary
- Statistical claims examined: 12
- Clean: 6
- Issues: 6  (2 causal overclaim · 1 bare-p · 1 missing-N · 1 mult-comparison · 1 cross-location mismatch)

## Findings (claim → issue → fix)
| # | Locator | Claim (quoted) | Issue | Fix (author action) |
|---|---|---|---|---|
| 1 | res ¶2 | "the gate caused an 18-pp gain in tool-call accuracy" | Causal verb on an uncontrolled ablation (MANUSCRIPT_MAP: not an RCT) | Soften to "was associated with an 18-pp gain"; or justify a causal design |
| 2 | res ¶3 | "tool-call accuracy was significantly higher (p = 0.03)" | Bare p; no effect size, no CI | Add point estimate + 95% CI + named test + N: "X pp (95% CI a–b; two-sample t-test, n = …)" |
| 3 | res ¶3 | "the gate reduced hallucinated tool calls (p = 0.02)" | N not stated for this comparison | State N for the comparison |
| 4 | res ¶5 | "the gate helped on 3 of 8 task subsets" | 8 subgroup tests, no correction, winners only | Report all 8; apply BH-FDR or justify; label exploratory if post-hoc |
| 5 | res ¶2 vs Table 2 | "21% → 6%" in text; Table 2 shows "21% → 8%" | Cross-location number mismatch | Reconcile to the real value; fix everywhere at once (Protected — confirm which is correct) |
| 6 | res ¶4 | "a significant improvement" | "significant" as a synonym for large | Give the magnitude with a number; reserve "significant" for the test result |

## Causation flags (design vs verb)
- Design per MANUSCRIPT_MAP: ablation comparison (not randomized at the unit of inference).
- Licensed: "associated with", "predicts", "co-occurs with". NOT: "causes", "drives", "the effect of".
- Finding 1 violates this — highest priority.

## Cross-location consistency
- Hallucinated-call rate: text 6% vs Table 2 8% — MISMATCH (finding 5).
- N: text "512 tasks" vs Methods "512 held-out" — consistent.
- 18-pp gain recomputes from Table 2 (39% → 57%) — OK.

## Notes for the author
- Findings 1, 5 are Protected (causal claim / changed number) — need your sign-off, not a silent edit.
- Confirm which test produced each p-value; I did not infer any (that would change what was reported).

End with the tally (clean / issues) and a one-line worst-risk. Never report a Results section "clean" while a bare p-value, a causal overclaim, or a cross-location mismatch stands.

Pairs With

• agent_docs/statistics.md — the checklist this skill executes; read it first, it is the authority for every rule above.
• integrity-reviewer agent — escalate when the signals look like patterned selective-reporting/HARKing across the whole manuscript, not isolated reporting gaps; it scans breadth, /stats-check verifies each number.
• /claim-check — run alongside: claim-check verifies verbs against cited sources, stats-check verifies numbers against the data and the design. Together they cover claims + quantities.
• citation-gate.sh (PostToolUse) — orthogonal (it checks \cite resolution), but run it so the Results section is structurally clean before the numerical pass.

Common Rationalizations

Rationalization	Reality
"p < 0.05 is the result; the effect size is obvious from the means"	A bare p hides magnitude and precision. Report the estimate + CI explicitly; do not make the reader reconstruct it.
"It's a significant improvement"	"significant" ≠ large. State the magnitude with a number; keep "significant" for the test.
"The gate improved accuracy" (from an ablation)	"improved" smuggles causation. An uncontrolled ablation licenses "was associated with". Match the verb to the design.
"I only report the comparisons that worked"	Reporting winners only is selective reporting. Disclose every test run; correct for the family.
"The abstract says 92%, the table says 89%, close enough"	A cross-location mismatch is a hard finding. The same quantity reads the same everywhere — recompute, do not eyeball.
"I'll just change the number to match"	Reconciling a clerical typo is fine; changing what was measured is a Protected Claim. Flag it, get sign-off.

Notes

• This skill never invents a number, a test, or an assumption. A missing value is [VALUE — verify]; an unknown test is a question for the author — per the cardinal rule in CLAUDE.md.
• Causation-vs-association and selective-reporting judgments are Reasoner-tier (CLAUDE.md → Model Selection); the cross-location number-matching is deterministic — recompute, do not estimate (CLAUDE.md → Model vs Code).
• A recurring statistics slip (the author keeps correcting "significant", or bare p-values) is a rule — log under tasks/reviews/, applies_to: [statistics], promote to ## Top Rules if it recurs (CLAUDE.md → Self-Improvement Loop).