Evaluating the Success of a Data Analysis
Abstract
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.
Cite
@article{arxiv.1904.11907,
title = {Evaluating the Success of a Data Analysis},
author = {Stephanie C. Hicks and Roger D. Peng},
journal= {arXiv preprint arXiv:1904.11907},
year = {2019}
}
Comments
16 pages