Statistical analysis is where AI assistance carries the highest risk in research. A coding error in your literature review wastes an hour; a statistical error in your analysis produces wrong results that can reach publication. This post establishes where the line is.
Where AI Genuinely Accelerates Statistical Work
Method selection orientation: describe your data structure and research question to Claude, ask which statistical tests are appropriate. This is useful orientation, not final method selection — you verify the recommendation against statistical textbooks or methodological papers in your field. Claude identifies relevant options you might not have considered; your expertise determines what’s appropriate.
Code generation: asking Claude or Copilot to write the code for a specific test (Mann-Whitney U test, linear mixed models, Cox proportional hazards) is legitimate use. The generated code is a template that you verify against the package documentation before running. Code errors are catchable; interpretation errors are not.
Interpretation assistance: after running your analysis and obtaining results, ask Claude to explain what the output means in plain terms. Useful for understanding output from unfamiliar packages or for checking your interpretation against an independent reading.
Where AI Creates Risk
Method justification: AI can argue persuasively for almost any statistical approach. Don’t use AI to justify a choice — use it to surface options, then apply your own expertise and field norms to select. Reviewers are subject-matter experts; AI-generated justifications for suboptimal methods won’t survive review.
Assumption checking: AI cannot check statistical assumptions on your actual data (normality, homoscedasticity, independence). These require running actual tests on your data. AI can tell you which assumptions to check, not whether they’re met.
The Verification Protocol
For every AI-generated statistical code: (1) read the code line by line before running, (2) run the test on a small simulated dataset with known results to verify it outputs what you expect, (3) cross-reference the function parameters against the package documentation. This 15-minute protocol prevents the most common errors.
