  Visualization supports exploratory data analysis (EDA), but EDA frequently
presents spurious charts, which can mislead people into drawing unwarranted
conclusions. We investigate interventions to prevent false discovery from
visualized data. We evaluate whether eliciting analyst beliefs helps guard
against the over-interpretation of noisy visualizations. In two experiments, we
exposed participants to both spurious and 'true' scatterplots, and assessed
their ability to infer data-generating models that underlie those samples.
Participants who underwent prior belief elicitation made 21% more correct
inferences along with 12% fewer false discoveries. This benefit was observed
across a variety of sample characteristics, suggesting broad utility to the
intervention. However, additional interventions to highlight counterevidence
and sample uncertainty did not provide significant advantage. Our findings
suggest that lightweight, belief-driven interactions can yield a reliable, if
moderate, reduction in false discovery. This work also suggests future
directions to improve visual inference and reduce bias. The data and materials
for this paper are available at https://osf.io/52u6v/
