Statistical significance
A measure of whether a selection-rate gap is large enough, given the sample size, to be unlikely to occur by chance. Courts have used a two-to-three standard-deviation benchmark (Castaneda v. Partida, 1977; Hazelwood School District v. United States, 1977). It complements the four-fifths rule: a small gap in a large pool can be significant, and a large ratio in a tiny pool may not be.
Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
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.
A measure of whether a selection-rate gap is large enough, given the sample size, to be unlikely to occur by chance. Courts have used a two-to-three standard-deviation benchmark (Castaneda v. Partida, 1977; Hazelwood School District v. United States, 1977). It complements the four-fifths rule: a small gap in a large pool can be significant, and a large ratio in a tiny pool may not be.
A measure of whether an observed difference between conditions is larger than what random variation alone would plausibly produce, without regard to whether that difference is large enough to matter in practice.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The hiring-AI problem: bias audits, notices, and the law already watching hiring AI · The EU AI Act: The Executive Map, Certified AI Governance Professional (CAIGP)
- Designing a clean experiment that proves something · Research, Benchmarking, and Staying at the Frontier, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Statistical significance 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.
Knows the rules on automated employment decisions, bias audits and notices, and treats hiring AI as the highest-risk use case it is.
One lesson on this page teach it as a primary outcome.
Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.
One lesson on this page teach it as a primary outcome.
Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.
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.
- Ethical AI Specialistneeds it at depth · 5 open postings this week
- AI Regulatory Counselneeds working knowledge · 14 open postings this week
- Responsible AI Leadneeds it at depth · 7 open postings this week
- AI Model Validatorneeds it at depth
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- Chief Privacy Officer, AI privacy editionneeds 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: NYC Local Law 144; EEOC guidance; EU AI Act Annex III employment; EU AI Act Articles 13 and 50.
One interview question about it
An AI tool screens job applicants. What must be true before you allow it, and what do you check every quarter?
Asked of candidates for ai in hiring and employment decisions. 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.
- Human resources and people operations carries it as a claim to back up: Fair-hiring review and disparate-impact awareness are HR discipline.
- A data science, statistics or analytics degree carries it as work you can show: Bias and failure modes were in your projects, not only your reading.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.