“Statistics: A Life Cycle View”

Statistical Modeling, Causal Inference, and Social Science 2022-08-18

This article from Ron Kenett is a few years old but is still relevant:

Statistics has gained a reputation as being focused only on data collection and data analysis. This paper is about an expanded view of the role of statistics in research, business, industry and service organizations. . . . a “life cycle view” consisting of: 1) Problem elicitation, 2) Goal formulation, 3) Data collection, 4) Data analysis, 5) Formulation of findings, 6) Operationalization of findings, 7) Communication and 8) Impact assessment. These 8 phases are conducted with internal iterations that combine the inductive-deductive learning process . . . The envisaged overall approach is that applied statistics needs to involve a trilogy combining: 1) a life cycle view, 2) an analysis of impact and 3) an assessment of the quality of the generated information and knowledge. . . .

It can be hard to write, and to read, this sort of article, as advice about problem elicitation, goal formulation, etc., can sound so vague compared to harder-edged topics such as optimization, computing, and probability theory. But all these things are important, and I think it does help to think them through, in specific examples and more generally.

Statistics is a branch of engineering.