Recognises a profiling sample excluding the problem population
A sample drawn from part of the population, read as the whole.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 80% | Not shown |
| gemini-3.5-flash-lite | Low cost | 74% | Not shown |
| gpt-6-luna | Low cost | 4% | Not shown |
| claude-haiku-5-5 | Low cost | 82% | Not shown |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The full population, or a sample stratified by the populations
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: PR-001 days_past_due sampled from the original-book extract; 8,412 migrated loans not in the frame Acceptable: Full population, or a sample stratified by book
Also known as: Sample excludes the problem population
Related knobits
Builds on
All 10 knobits of Profile and assess data quality.
Source: Review checklist 04-profiling-assessment, row 2 (DMBOK §2.4)