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Statistical models should accurately reflect analysts' domain knowledge aboutvariables and their relationships. While recent tools let analysts expressthese assumptions and use them to produce a resulting statistical model, itremains unclear what analysts want to express and how externalization impactsstatistical model quality. This paper addresses these gaps. We first conduct anexploratory study of analysts using a domain-specific language (DSL) to expressconceptual models. We observe a preference for detailing how variables relateand a desire to allow, and then later resolve, ambiguity in their conceptualmodels. We leverage these findings to develop rTisane, a DSL for expressingconceptual models augmented with an interactive disambiguation process. In acontrolled evaluation, we find that rTisane's DSL helps analysts engage moredeeply with and accurately externalize their assumptions. rTisane also leads tostatistical models that match analysts' assumptions, maintain analysis intent,and better fit the data.

rTisane: Externalizing conceptual models for data analysis increases engagement with domain knowledge and improves statistical model quality