(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Yu and Kumbier write:

Artificial intelligence (AI) is intrinsically data-driven. It calls for the application of statistical concepts through human-machine collaboration during generation of data, development of algo- rithms, and evaluation of results. This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population, question of interest, representativeness of training data, and scrutiny of results (PQRS).

All 4 of the ideas in PQRS come from classical statistics: Population is the basis of sampling inference, Question comes up in defining causal inference, Representativeness is also central to sampling, and Scrutiny relates to hypothesis testing and exploratory data analysis. So by mentioning PQRS, they’re really saying that AI can be improved using long-established statistical principles. Or, to put it another way, that long-established statistical principles can be made more useful through AI techniques.

The post Bin Yu and Karl Kumbier: “Artificial Intelligence and Statistics” appeared first on Statistical Modeling, Causal Inference, and Social Science.

**Please comment on the article here:** **Statistical Modeling, Causal Inference, and Social Science**