Journal of Data Science

In data analysis, unexpected results often prompt researchers to revisit their procedures to identify potential issues. While some researchers may struggle to identify the root causes, experienced researchers can often quickly diagnose problems by checking a few key assumptions. These checked assumptions, or expectations, are typically informal, difficult to trace, and rarely discussed in publications. In this paper, we introduce the term analysis validation checks to formalize and externalize these informal assumptions. We then introduce a procedure to identify a subset of checks that best predict the occurrence of unexpected outcomes, based on simulations of the original data. The checks are evaluated in terms of accuracy, determined by binary classification metrics, and independence, which measures the shared information among checks. We demonstrate this approach with a toy example using step count data and a generalized linear model example examining the effect of particulate matter air pollution on daily mortality.

A challenge that data scientists face is building an analytic product that is useful and trustworthy for a given audience. Previously, a set of principles for describing data analyses were defined that can be used to create a data analysis and to characterize the variation between analyses. Here, we introduce a concept called the alignment of a data analysis, which is between the data analyst and an audience. We define an aligned data analysis as the matching of principles between the analyst and the audience for whom the analysis is developed. In this paper, we propose a model for evaluating the alignment of a data analysis and describe some of its properties. We argue that more generally, this framework provides a language for characterizing alignment and can be used as a guide for practicing data scientists to building better data products.