  Visual analytics (VA) tools support data exploration by helping analysts
quickly and iteratively generate views of data which reveal interesting
patterns. However, these tools seldom enable explicit checks of the resulting
interpretations of data -- e.g., whether patterns can be accounted for by a
model that implies a particular structure in the relationships between
variables. We present EVM, a data exploration tool that enables users to
express and check provisional interpretations of data in the form of
statistical models. EVM integrates support for visualization-based model checks
by rendering distributions of model predictions alongside user-generated views
of data. In a user study with data scientists practicing in the private and
public sector, we evaluate how model checks influence analysts' thinking during
data exploration. Our analysis characterizes how participants use model checks
to scrutinize expectations about data generating process and surfaces further
opportunities to scaffold model exploration in VA tools.
