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Proper statistical modeling incorporates domain theory about how conceptsrelate and details of how data were measured. However, data analysts currentlylack tool support for recording and reasoning about domain assumptions, datacollection, and modeling choices in an integrated manner, leading to mistakesthat can compromise scientific validity. For instance, generalized linearmixed-effects models (GLMMs) help answer complex research questions, butomitting random effects impairs the generalizability of results. To addressthis need, we present Tisane, a mixed-initiative system for authoringgeneralized linear models with and without mixed-effects. Tisane introduces astudy design specification language for expressing and asking questions aboutrelationships between variables. Tisane contributes an interactive compilationprocess that represents relationships in a graph, infers candidate statisticalmodels, and asks follow-up questions to disambiguate user queries to constructa valid model. In case studies with three researchers, we find that Tisanehelps them focus on their goals and assumptions while avoiding past mistakes.

Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships