Survivorship Bias
A selection pattern in which the visible sample (customers still using and publicizing a product) is skewed toward success, because customers who abandoned or scaled back the same product tend not to appear in the visible record at all. A common risk when relying on a vendor's published case studies.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Engineering Judgment and Professional Formation.
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 selection pattern in which the visible sample (customers still using and publicizing a product) is skewed toward success, because customers who abandoned or scaled back the same product tend not to appear in the visible record at all. A common risk when relying on a vendor's published case studies.
A distortion that occurs when only the "survivors" or successes in a group are visible or counted, such as a productivity-gain statistic drawn only from companies willing to report success, undercounting adopters who failed or saw no gain and are less likely to appear in the sample.
A data-quality issue where analysis includes only "survivors" (successful cases) and excludes failures, skewing results.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Interconnected Literacies: How Digital, Data, and AI Literacy Connect · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- The Economic Impact Report with Numbers That Survive Scrutiny · AI Economics and Impact Modeling, The AI Lobbyist: Certified AI Policy Strategist
- Proving a robot saves or makes money · The Economics of Robots, the Business Case, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Survivorship Bias 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.
Builds an honest value model: baseline, measured change, cost, risk, and the projects that should be stopped.
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.
- Chief AI Officerneeds it at depth
- AI Adoption and Enablement Leadneeds working knowledge
- AI Program Managerneeds working knowledge · 11 open postings this week
- Operations Manager, AI-readyneeds working knowledge
- Chief Information Officer, technology leadership editionneeds working knowledge
- AI Product Manager, responsible product editionneeds working knowledge · 3 open postings this week
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.
One interview question about it
How would you measure the return on an AI deployment honestly, including the costs people prefer to leave out?
Asked of candidates for roi and value measurement for ai. 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.
- A business, MBA or technology management degree carries it as a claim to back up: Value and return measurement are a course you passed.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.