Recognises a quality trend read without its population change
A movement explained by quality when the population changed.
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 | 86% | Not shown |
| gemini-3.5-flash-lite | Low cost | 79% | Not shown |
| gpt-6-luna | Low cost | 86% | Not shown |
| claude-haiku-5-5 | Low cost | 87% | 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 population change stated and the like-for-like series
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: SC-002 99.6% in May to 97.9%, "loan servicing data entry deteriorating"; 8,412 loans migrated in June Acceptable: Original book flat at 99.6%; migrated 86.9% since they arrived
Also known as: Trend shown without the change in population
Related knobits
Builds on
- Q-754 Explains like-for-like trends of quality measures
- Q-1901 Explains a pooled rate as the weighted mean of its population rates
All 10 knobits of Set data quality service levels and the scorecard.
Source: Review checklist 07-sla-scorecard, row 3 (25024 (stated population); DMBOK)