CORE GUIDE
Verification for AI-assisted data analysis
AI can accelerate extraction, transformation and interpretation, but every important numerical conclusion should be reproducible from explicit data and calculations outside the model's narrative.
Mental model
Treat the model as an analysis collaborator, not the calculator of record. Reliable analysis separates source data, deterministic transformations, statistical assumptions and narrative interpretation so each layer can be checked independently.
Why it matters
Models are good at proposing formulas and explaining patterns, but they can silently misread units, omit rows, choose the wrong denominator or invent a plausible number. When the output becomes a business decision, a fluent explanation is insufficient. The calculation path must be rerunnable and key assertions should link back to concrete rows or aggregates.
01
Create a reproducible evidence chain
Keep raw inputs immutable, express transformations in code or auditable formulas, and record assumptions such as missing-value handling and grouping logic. Ask AI to propose or explain analysis, then execute the calculation in a deterministic environment. Use spot checks, invariants and independent recomputation for consequential metrics before writing the narrative.
02
Example: conversion rate uses the wrong denominator
An AI summary says conversion improved from 12% to 15%, but it divided purchases by sessions in one period and by users in another. A reproducible notebook exposes the denominator mismatch immediately. The corrected calculation changes the conclusion, and the narrative is regenerated from verified metrics rather than preserved because it sounded convincing.
Common failure modes
- Copying numbers from model prose without rerunning the calculation.
- Changing data-cleaning assumptions between comparison periods.
- Treating a chart that looks plausible as verification of the underlying aggregation.
Engineering heuristics
- Make important metrics reproducible with code or explicit formulas.
- Check units, denominators, joins and missing-data policy before interpreting trends.
- Trace each consequential claim back to a verified table or calculation.
Takeaways
- 01Narrative quality cannot validate arithmetic.
- 02Deterministic recomputation anchors AI-assisted analysis.
- 03Verification should cover both data transformation and interpretation assumptions.
Reading evidence
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