Neglected, Acknowledged, or Targeted: A Conceptual Framing of Variability, Data Analysis, and Domain Consequences

Abstract

Variability is underemphasized in domains such as engineering. Statistics and data science education research offers a variety of frameworks for understanding variability, but new frameworks for domain applications are necessary. This study investigated the professional practices of working engineers to develop such a framework. The Neglected, Acknowledged, or Targeted (NAT) Taxonomy describes whether an analyst’s data analysis choices engage with variability, and whether those choices target the domain application consequences of variability. A targeted analysis is the most beneficial rung for engineering applications and is therefore a useful concept for instruction. This study describes the qualitative methods used to develop the NAT Taxonomy and describes how the taxonomy can be used in statistics and data science education, particularly in support of other domain applications.