Statistics: A Life Cycle View
17 Pages Posted: 26 Aug 2013 Last revised: 28 Apr 2016
Date Written: 2014
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. Such an approach provides an antidote to the narrow view of statistics outlined above. The life cycle view we elaborate on can contribute to close the gap between theory and practice and improve the position of statistics as a scientific discipline with wide relevance to organizations and research activities. Specifically we discus here 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 studied by George Box (Box, 1997). Covering these 8 dimensions, beyond the data analysis phase, increases the impact of statistical analysis and enhances the level of generated knowledge and information quality it leads to. 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. We begin with a section introducing the problem, continue with a review of the InfoQ concept presented in Kenett and Shmueli (2013) and proceed with a description of the eight life cycle phases listed above. Adopting a life cycle view of statistics has obvious implications to research, education and statistical practice. We conclude with a discussion of such implications.
Keywords: Life Cycle View of Statistics, Impact Analysis, Knowledge Generation, PSE, InfoQ
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