Explains statistical process control for data quality
A process measured over time on a control chart with a mean and control limits: variation within the limits has common causes, a point outside them or a run signals a special cause to investigate; improvement removes special causes first, then reduces variation.
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 | 44% | Not shown |
| gemini-3.5-flash-lite | Low cost | 44% | Not shown |
| gpt-6-luna | Low cost | 44% | Not shown |
| claude-haiku-5-5 | Low cost | 44% | 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: A process measured over time on a control chart with a mean and control limits: variation within the limits has common causes, a point outside them or a run signals a special cause to investigate; improvement removes special causes first, then reduces variation.
How it is assessed: Answers a CDMP-style multiple-choice question on the statistical process control
All knobits of Data quality.
Source: DAMA-DMBOK: Data Management Body of Knowledge, 2nd edition (revised), chapter 13: Data Quality, element QV-28