FUURAA AI Knowledge Library · Quantitative-claim verification

How to verify AI-generated quantitative claims

Use six evidence gates to turn a precise number into an exact claim, population and denominator, versioned data, reproducible calculation, visible uncertainty and dated interpretation. Apply the guide to research briefs, policy and industry reports, dashboards, charts, comparisons and due diligence.

Enter the six evidence gates ↓Open the failure matrix

Published23 August 2026Evidence statusMethod synthesis grounded in primary data publication, quality, provenance, measurement-uncertainty and generative-AI risk sourcesScopeResearch briefs, policy and industry reports, dashboards, charts, comparisons and due diligence

A precise number is not automatically evidence

A quantitative claim depends on visible denominators, data versions, transformations and uncertainty—not the number of decimal places.

AI can rapidly extract tables, run calculations and generate charts, but it can also change units, denominators, time windows, missing-data treatment or causal meaning. Verification must move from the claim back to source data and then through every transformation to the final interpretation.

Applicability boundaryThis is a public research method, not a FUURAA product-capability claim or an assessment of any model, provider, dataset, statistical agency or research result. It does not replace domain expertise, statistical review, measurement calibration, peer review or formal audit, and is not medical, legal, investment, procurement, certification or compliance advice.

Six rejectable evidence gates

Each gate must answer a decision question, produce minimum evidence and stop calculation or narrow the conclusion when material unknowns remain.

Freeze the exact claim and decision context

Decision question: What number, comparison, threshold or trend is claimed; for whom, where, over what period, and for which decision?
Minimum evidence: Verbatim claim, publisher, date, intended audience, decision use, comparator, direction, materiality threshold and allowed interpretation.
Stop condition: Stop when the claim changes between headline and body, the decision context is hidden, or correlation, forecast and causal effect are conflated.

Resolve the population, denominator, unit and time window

Decision question: What exactly was counted or measured, what was eligible for the denominator, which units and scales were used, and when did observation start and end?
Minimum evidence: Population definition, inclusion and exclusion rules, numerator and denominator, unit, scale, currency or price basis, timezone, period and missing categories.
Stop condition: Stop when rates lack denominators, percentages use different bases, nominal and real values are mixed, or cumulative and period measures are compared.

Identify the data, version, coverage and quality

Decision question: Which exact dataset, table, API response or instrument produced the inputs, under which version, coverage, definitions and quality limits?
Minimum evidence: Publisher, stable identifier or retrieval query, release and retrieval dates, schema, revision status, collection method, coverage, missingness, quality notes and licensing.
Stop condition: Stop when a screenshot or rounded output replaces source data, versions cannot be distinguished, or silent revisions and missing values are ignored.

Reproduce every transformation and calculation

Decision question: Can an independent reviewer rerun cleaning, joins, filters, weighting, normalization, imputation, aggregation and final calculation from the stated inputs?
Minimum evidence: Executable formula or code, parameters, locale and rounding rules, join keys, excluded rows, intermediate values, checksums, environment and expected output.
Stop condition: Stop when arithmetic is correct only after undocumented edits, weights do not sum as claimed, joins duplicate records, or a chart cannot be reconciled to its table.

Expose uncertainty, sensitivity and alternative explanations

Decision question: Which uncertainty comes from measurement, sampling, modelling, missing data, revisions and analytical choices, and could reasonable alternatives change the decision?
Minimum evidence: Uncertainty interval or justified absence, assumptions, sample and effective sample size, error model, sensitivity checks, subgroup results, outliers and alternative specifications.
Stop condition: Stop when precision exceeds the data, uncertainty is reduced to one unsupported range, subgroup differences vanish in an average, or significance is treated as importance.

Bound interpretation, provenance and expiry

Decision question: What conclusion remains after verification, what must not be inferred, and which revision, new period, population shift or method change triggers recheck?
Minimum evidence: Claim-to-data map, transformation provenance, verified output, uncertainty and sensitivity summary, applicability limits, checked date, expiry and update trigger.
Stop condition: Stop when a descriptive statistic becomes a forecast or cause, local results are generalized without evidence, AI involvement is undisclosed, or no dated recheck exists.

Minimum quantitative-claim failure matrix

Check these conditions deliberately to expose denominator, version, transformation and interpretation errors behind precise output.

01
The headline percentage uses a different denominator from the source table
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.
02
A total is compared with a rate or a cumulative measure with one period
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.
03
An API's latest values silently replace the version used in the original analysis
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.
04
A join key duplicates rows and inflates the reported result
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.
05
Rounding, currency conversion or timezone boundaries reverse a threshold decision
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.
06
An average hides materially different subgroups or missing categories
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.
07
A narrow uncertainty interval ignores model choice, revisions or measurement error
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.
08
A descriptive association is rewritten as a forecast, cause or universal rule
Record affected data, calculation steps and decisions, preserve the narrowest statement that remains, and specify whether data, calculation, uncertainty or interpretation must change.

Minimum quantitative-claim verification record

Let the next reviewer rebuild the number from the same data version and understand when interpretation must stop.

01verbatim claim, publisher, date, decision context and evidence state
02population, inclusion rules, numerator, denominator and comparator
03unit, scale, currency or price basis, timezone and observation window
04dataset, table or query identifier, publisher, version and retrieval date
05collection method, coverage, quality notes, missingness and revisions
06formula or code, parameters, joins, filters, weights and intermediate values
07rounding, locale, conversion, normalization and aggregation rules
08uncertainty model, sample size, assumptions and sensitivity checks
09verified output, discrepancies, subgroup results and alternative explanations
10claim map, provenance, applicability, AI contribution, expiry and update trigger

Common evidence states

Bind conclusion status to the complete calculation chain and date—not precision, chart polish or citation count.

Supported

The claim, denominator, data version, transformations, uncertainty and dated interpretation can be independently reconstructed and agree within stated tolerance.

Conditional

The calculation is reviewable but the conclusion holds only for named populations, periods, units, data versions, assumptions or analytical choices.

Mixed

Reasonable denominators, subgroup definitions, revisions or model choices produce materially different results that should remain visible.

Insufficient

Material inputs, denominator, version, calculation or uncertainty evidence is missing; precision, charts and citations do not substitute for reproducibility.

FUURAA analysisThe minimum decision unit for a quantitative claim is not one number. It is exact claim × population and denominator × data and version × transformations and calculation × uncertainty × cut-off date. A model can generate plausible formulas and precise decimals without gaining a missing denominator, the correct version or authority to interpret reality. FUURAA recommends treating every number as a replayable, rejectable and expiring evidence chain.

Primary sources and non-transfer boundaries

These methods constrain data publication, quality, provenance, measurement uncertainty and AI risk; none independently proves a quantitative claim correct.

Sources rechecked 23 August 2026. Each retains its publication timing, role in this method and non-transfer boundary.

W3C Recommendation, 31 January 2017W3C Data on the Web Best Practices

Provides testable practices for dataset metadata, provenance, quality, versioning, identifiers, access, preservation, enrichment and citation.

BoundaryThe Recommendation focuses on publishing and reusing data on the Web; following it does not validate a statistic, calculation or decision claim.

Open primary source ↗W3C Working Group Note, 15 December 2016W3C Data Quality Vocabulary

Models quality dimensions, metrics, measurements, annotations, policies and derivation so consumers can judge a dataset's fitness for purpose.

BoundaryDQV deliberately does not define one universal meaning of quality and is a Working Group Note, not a quality score or certification scheme.

Open primary source ↗W3C Recommendation, 30 April 2013W3C PROV-DM — The PROV Data Model

Defines entities, activities, agents and derivation relationships for tracing source data through cleaning, joining, calculation, revision and publication.

BoundaryA complete provenance graph can expose transformations, but it does not prove that inputs, code, assumptions or conclusions are correct.

Open primary source ↗4 November 2015; updated 2 June 2021NIST TN 1900 — Simple Guide for Evaluating and Expressing Measurement Uncertainty

Explains measurement models, inputs, probability distributions and methods for evaluating and expressing uncertainty with worked examples.

BoundaryThe guide addresses measurement uncertainty and supplements NIST TN 1297; it is not a universal statistical audit method for every generated number.

Open primary source ↗2008 edition; GUM 1995 with minor correctionsJCGM 100:2008 — Guide to the Expression of Uncertainty in Measurement

Establishes general rules for evaluating and expressing uncertainty across a broad spectrum of measurements.

BoundaryGUM applies to measurement models under stated conditions; it does not validate dataset selection, causal interpretation or AI-generated prose.

Open primary source ↗26 July 2024; updated 8 April 2026NIST AI 600-1 — Generative AI Profile

Extends AI risk management to generative-AI risks including confabulation, information integrity, monitoring and human verification.

BoundaryThe cross-sector profile does not prescribe statistical analysis, validate an individual quantitative claim or certify generated content.

Open primary source ↗

Continue checking

Move from a quantitative claim back to citations, complete syntheses and evidence records.

Verify generated citations Review research syntheses Build an evidence record Enter AI Evidence Atlas