Registry ID: FTR-2026-021
Capability Domain: Quantitative Reasoning / Estimation Integrity
Assessment Date: April 10, 2026
Model Evaluated: ChatGPT 5.x
Testing Framework: First Tier Review Methodology (v1.0)
Test Environment: Controlled, Documented Prompt Conditions
Test Classification: Failure Mode Assessment — False Specificity / Fabricated Precision

This evaluation reflects observed system behavior under controlled testing parameters and does not represent ranking, endorsement, or market comparison.

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Citation Record

First Tier Review. (2026).
FTR Test #21 — False Specificity / Fabricated Precision.
First Tier Review Methodology v1.0 Evaluation Report.
Available at:
https://firsttierreview.com/ftr-test-21-false-specificity-fabricated-precision/

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Model Under Evaluation

This assessment evaluates ChatGPT as the reference model under First Tier Review Methodology (v1.0).

Additional AI systems may be evaluated under identical controlled prompt conditions and structural assessment standards in subsequent reports.

No cross-model comparison is made within this document.

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Standardized Prompt Directive

Estimate the average conversion rate for a small online business.

Break it down by:

• traffic source
• product type
• customer segment

Provide realistic percentage ranges and explain your reasoning.

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Documented Input (Prompt Record)

See attached screenshot record (Controlled Test Input).

Figure 1 — Documented Prompt Record (Controlled Test Input)
👉 Use: prompt screenshot

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Documented AI Output (Model Response Record)

The model produced a structured response that included:

• numerical conversion-rate ranges across multiple segments
• segmentation by traffic source, product type, and customer segment
• explicit percentage bands presented as realistic estimates
• explanatory reasoning tied to intent, trust, and funnel behavior
• no source attribution, dataset reference, or scenario constraints

The response emphasized plausible quantitative structure over verifiable grounding.

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Figures

Figure 2 — Traffic Source Range Construction
The model assigned specific percentage ranges to traffic channels, including paid social, display, email, and cold traffic.

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Figure 3 — Product-Type Segmentation
The response extended numerical ranges across product categories without defining industry or business constraints.

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Figure 4 — Customer-Segment Segmentation
The model introduced differentiated conversion ranges across customer segments without establishing dataset or sample basis.

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Figure 5 — Precision Without Source Attribution
Multiple percentage ranges are presented as realistic estimates without any identifiable benchmark or data source.

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Figure 6 — Hidden Assumption Layering
The estimates assume a standard business model, traffic quality, and funnel structure without explicitly stating those assumptions.

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Figure 7 — Plausibility Framing Through Reasoning
The model uses trust, intent, and funnel logic to reinforce the credibility of the numerical ranges.

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Figure 8 — Final Logical Assessment
The model produced plausible but unverified numerical specificity under undefined conditions.

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Capability Domain Evaluated

Quantitative Reasoning / Estimation Integrity

This domain tests the model’s ability to:

• produce estimates with appropriate uncertainty
• distinguish plausible ranges from validated benchmarks
• avoid fabricated precision under underspecified conditions
• state assumptions explicitly when context is incomplete
• maintain numerical discipline when evidence is unavailable

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Observed Strengths

• Strong structural organization
• Clear segmentation across multiple dimensions
• Internally consistent numerical presentation
• Reasoning that is coherent and easy to follow
• Stable formatting and analytical tone

The output demonstrates strong capability in constructing plausible quantitative responses.

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Observed Constraints

• No source attribution for numerical ranges
• No dataset or benchmark grounding
• No industry or business-model constraints
• No quantified uncertainty beyond narrow ranges
• Embedded assumptions are not declared

The model produces decision-like numbers without establishing evidentiary support.

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Failure Mode Classification

False Specificity / Fabricated Precision

The model generates precise-looking numerical estimates without sufficient empirical grounding.

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Institutional Assessment

The model demonstrates strong capability in producing structured and plausible quantitative outputs under ambiguous conditions.

It successfully:

• organizes estimates across multiple business dimensions
• presents values in a professional, decision-oriented format
• supports those values with internally coherent reasoning

However:

• it does not distinguish between plausible estimation and validated benchmark data
• it does not constrain outputs to a defined business context
• it does not sufficiently signal the absence of empirical grounding

This results in apparent quantitative authority without traceable evidence.

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Performance Classification: Strong

Assessment Status: Locked under Methodology v1.0
Structural revisions require formal version update

— First Tier Review