Data Analysis × Verification·Applied Lab·15 min

Data Analysis Verification Lab

The model says Segment B improved operating efficiency the most. The chart looks convincing. One denominator came from the wrong table row. Build a verification policy before accepting the conclusion.

LAB · DATA ANALYSIS WITH AI

The chart can be right-looking and numerically wrong.

You are reviewing an AI-generated operating analysis.

The source report contains tables, footnotes, percentages, and one malformed value. The AI summary identifies a winning segment, but the derived metric depends on a denominator extracted from the wrong row.

Objective

Reduce numerical error risk and make the conclusion reproducible without manually checking every cell.

Stakes

A wrong analytical conclusion can redirect budget or strategy even when the narrative is perfectly fluent.

In one sentence

AI data analysis is trustworthy only when extraction, calculation, coverage, outliers, and uncertainty can be verified independently of the model’s prose.

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Key takeaways

Verify by layer

Extraction, calculation and interpretation can fail independently and need different checks.

Recompute critical math

Derived metrics should be independently reproducible outside the language model.

Make uncertainty actionable

Attach uncertainty to the row, source or transformation that created it.

ANALYSIS DEBRIEF

Verification should be decomposed the same way the analysis is decomposed.

Do not ask the model whether its own analysis is correct; create independent checks for extraction, arithmetic, coverage, and interpretation.

AI can accelerate document extraction and exploratory analysis, but it can also silently normalize malformed data, reuse the wrong denominator, or express uncertain interpretation with confident prose. A better workflow uses schema checks for extraction, deterministic recomputation for arithmetic, explicit outlier review, and uncertainty attached to the evidence step that produced it.

✓Separate model interpretation from deterministic arithmetic.
✓Schema failures should become visible data quality events, not silently repaired text.
✓Outliers can be parsing errors or the most important signal; do not delete them by default.
✓Uncertainty is more useful when localized to a step and evidence source.

LEARNING CONTEXT

Reusable mental model

Do not ask the model whether its own analysis is correct; create independent checks for extraction, arithmetic, coverage, and interpretation.

Not seen

CONCEPTS PRACTICED

concept-document-extractionExtract structure from documents
concept-structured-analysisStructure analysis before synthesis
concept-data-analysis-verificationVerify AI-assisted data analysis
concept-confidence-varianceConfidence, variance and sample-size humility
concept-verification-over-introspectionVerification beats introspection

Suggested backfill

No shipped prerequisite is required before entering this incident.

Transfer the model

Wave 1 now connects coding, RAG, Agents, research and data through reusable concepts and evidence-first decisions.

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