  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.
