Writing WorkflowMay 23, 2026

Statistical Assumption Check with Claude: Catching Errors Before Reviewers Do

I once missed a heterogeneity diagnostic in the pilot draft of a meta-analysis. It was an embarrassing oversight — Claude flagged it in under 30 seconds when I asked the right question.

Most researchers focus on the p-values, but reviewers focus on the assumptions behind those p-values. If you used a t-test but didn't check for normality, or a Cox model but didn't verify the proportional hazards assumption, your results are technically indefensible — even if the analysis is otherwise correct.

This post is part of the pre-submission audit workflow described in Self-Peer-Review with AI: A 5-Step Manuscript Audit Workflow. Here I'm going deeper on Step 2 — the statistical sanity check — with the exact prompt template and a worked example.

Why LLMs Are Surprisingly Good at This

LLMs don't calculate statistics. They can't run your data. But they are exceptionally good at two things that matter for assumption checking:

Pattern matching against standards. LLMs have been trained on every statistics textbook, methods paper, and reporting guideline you've ever read. They know that a random-effects meta-analysis requires a test for heterogeneity. They know that a repeated-measures ANOVA requires a sphericity check. They know STROBE and CONSORT by section number.
Finding what isn't there. A methods section is a protocol. An LLM can compare that protocol against a standard checklist and identify the gaps — which is exactly what a biostatistics reviewer does.

The limitation is that the LLM cannot verify whether your assumptions actually hold — only whether you tested them and reported the results. That's what you need for peer review purposes.

The Assumption Auditor Prompt

Don't ask "Is my stats okay?" That's too open-ended. Use this structured prompt instead:

"I am providing the Methods and Results sections of a clinical manuscript. Please do the following:

1. List every statistical test mentioned in the Methods section. 2. For each test, enumerate the required statistical assumptions (e.g., normality, homogeneity of variance, independence of observations, proportional hazards, etc.). 3. Search the Results section for explicit evidence that each assumption was tested (e.g., Shapiro-Wilk, Levene's test, Durbin-Watson, visual inspection of residuals, Bartlett's test). 4. Generate a table with three columns: [Statistical Test], [Required Assumption], [Tested in Results: Yes / No / Not Mentioned]. 5. For any 'No' or 'Not Mentioned' rows, rate the severity of the omission: HIGH (peer review will likely require this), MEDIUM (best practice but not always required), or LOW (minor)."

Run this with your Methods and Results pasted directly into the prompt. The output is an assumption checklist with severity ratings. Fix all HIGH items before submission.

Worked Example: A Meta-Analysis Draft

In that draft, the table looked like this after running the audit:

| Statistical Test | Required Assumption | Tested in Results? | |---|---|---| | Random-effects meta-analysis | Normality of effect size distribution | Not Mentioned | | Random-effects meta-analysis | Absence of publication bias | Not Mentioned (funnel plot not reported) | | Pooled proportion | Adequacy of sample sizes per study | Not Mentioned | | Sensitivity analysis | Robustness to influential studies | Not Mentioned |

Every "Not Mentioned" row was a gap. I added:

A Cochran Q test and I² statistic with interpretation
A funnel plot with Egger's test for publication bias
A leave-one-out sensitivity analysis

The paper passed peer review without a single comment on the statistical methodology. That's the outcome you're aiming for.

High-Severity Omissions by Test Type

These are the gaps that most reliably generate reviewer requests. Check these manually even if the LLM misses them:

t-test (independent / paired):

Normality check (Shapiro-Wilk for n<50, Q-Q plot for larger samples)
For independent: homogeneity of variance (Levene's test)

ANOVA (one-way, two-way, repeated-measures):

Normality check per group
Homogeneity of variance (Levene's)
For repeated-measures: sphericity (Mauchly's test; report Greenhouse-Geisser correction if violated)

Logistic regression:

Absence of perfect separation
Independence of observations (no clustering)
For large models: multicollinearity (VIF)

Cox proportional hazards:

Proportional hazards assumption (Schoenfeld residuals or log-log plot)

Meta-analysis:

Heterogeneity (Cochran Q, I²)
Publication bias (funnel plot + Egger's or Begg's test when k≥10)
Sensitivity analysis (leave-one-out)

Integrating This Into Your Revision Workflow

The best time to run this audit is immediately before you send the draft to co-authors for final review — not after. Here's why: co-authors who see a clean assumption table will approve faster. Co-authors who notice a missing diagnostic will ask you to fix it anyway, just with more email overhead.

Run the audit, fix the gaps, then circulate. The assumption table itself can become a supplementary checklist in your revision letter if reviewers raise statistical questions.

For journals requiring STROBE, CONSORT, or PRISMA compliance, the AVR Paper Checker at aiforacademic.world/avr formalizes this loop — it audits your manuscript against the relevant reporting standard automatically and generates a compliance table you can attach to your submission.

The Bottom Line

Statistical reviewers are not trying to reject your paper — they're trying to ensure the conclusions follow from defensible methods. Giving them an assumption checklist they don't have to build themselves is the fastest way to earn their trust.

Run the Assumption Auditor prompt before every submission. Fix the HIGH items. Document the MEDIUM items in your Limitations. The 30 minutes this takes is the cheapest insurance against a statistics-driven major revision you'll ever find.