Inside Out: Externalizing Assumptions in Data Analysis as Validation Checks

Music City Center 

In data analysis, unexpected results often prompt researchers to revisit their proce- dures 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 simula- tions of the original data. The checks are evaluated in terms of accuracy, determined by binary classification metrics, and independence, which measures the shared in- formation 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.

data analysis

data validation

diagnostics 

Section on Statistical Computing