False precision
Presenting an estimate with more apparent exactness (an unhedged, decimal-specific figure) than the underlying evidence actually supports, which invites scrutiny the number cannot survive and damages the credibility of the entire memo.
Defined in 3 GAGE programs, which carry 5 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
Presenting an estimate with more apparent exactness (an unhedged, decimal-specific figure) than the underlying evidence actually supports, which invites scrutiny the number cannot survive and damages the credibility of the entire memo.
Citing a specific number or benchmark score without being able to explain where it came from or what it actually measures, treated by this topic as more damaging to credibility than citing no number at all.
A number implying more accuracy or comparability than its inputs support, such as the product of two ordinal ratings, or a percentage reported to one decimal place from a sample of twelve.
Presenting an estimate as a single confident point figure when the underlying method cannot actually support that level of certainty; the single most common target of a hostile economic review.
A figure stated more precisely than its source can support, which asserts a resolution the measurement does not have.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The cost of bad data: pricing last quarter's silent drift in real money · The Money of Data, Certified AI Data Governance Professional (CADGP)
- The Economic Impact Report with Numbers That Survive Scrutiny · AI Economics and Impact Modeling, The AI Lobbyist: Certified AI Policy Strategist
- Holding the Room with Engineers: The Technical Credibility Defense · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
- Your portfolio, and what an artifact has to earn · Your First Week in Governance, Risk and Compliance, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
- Data quality and metric honesty · Governance, Risk and Compliance Metrics, Analytics, and Board Communication, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
Where it shows up at work
A definition is where False precision starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.
One lesson on this page teach it as a primary outcome.
Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
One lesson on this page teach it as a primary outcome.
Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Auditorneeds it at depth · 9 open postings this week
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Governance Engineerneeds it at depth · 7 open postings this week
- AI Model Validatorneeds it at depth
- Chief Audit Executive, AI audit editionneeds it at depth
- Senior AI Compliance Analystneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Articles 11 and 12; ISO/IEC 42001 Clause 7.5; EU AI Act Article 10; NIST AI RMF MAP 4.
One interview question about it
What evidence would you have ready before an auditor asks about an AI system, and how do you produce it as a byproduct of the work?
Asked of candidates for evidence collection and audit-ready documentation. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Compliance and regulatory affairs carries it as work you can show: Audit-ready documentation is what you produce.
- Internal audit and IT audit carries it as work you can show: Evidence evaluation is the profession.
- Project and program management carries it as a claim to back up: Decision logs and repositories are familiar.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.