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