2019/04/26 by Stephanie C. Hicks, Roger D. Peng, Hicks, Stephanie C. +1
Computer Science · Mathematics · #Applications (stat.AP) #Data Analysis with R #FOS: Computer and information sciences #Other Statistics (stat.OT) #Statistics Education and Methodologies #stat.AP #stat.OT
paper · pdf · doi:10.48550/arxiv.1904.11907
16 pages
arxiv created 2019/04/26 · openalex publication_date 2019/04/26 · arxiv updated 2019/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A fundamental problem in the practice and teaching of data science is how to evaluate the quality of a given data analysis, which is different than the evaluation of the science or question underlying the data analysis. Previously, we defined a set of principles for describing data analyses that can be used to create a data analysis and to characterize the variation between data analyses. Here, we introduce a metric of quality evaluation that we call the success of a data analysis, which is different than other potential metrics such as completeness, validity, or honesty. We define a successful data analysis as the matching of principles between the analyst and the audience on which the analysis is developed. In this paper, we propose a statistical model and general framework for evaluating the success of a data analysis. We argue that this framework can be used as a guide for practicing data scientists and students in data science courses for how to build a successful data analysis.