  Data visualizations are vital to scientific communication on critical issues
such as public health, climate change, and socioeconomic policy. They are often
designed not just to inform, but to persuade people to make consequential
decisions (e.g., to get vaccinated). Are such visualizations persuasive,
especially when audiences have beliefs and attitudes that the data contradict?
In this paper we examine the impact of existing attitudes (e.g., positive or
negative attitudes toward COVID-19 vaccination) on changes in beliefs about
statistical correlations when viewing scatterplot visualizations with different
representations of statistical uncertainty. We find that strong prior attitudes
are associated with smaller belief changes when presented with data that
contradicts existing views, and that visual uncertainty representations may
amplify this effect. Finally, even when participants' beliefs about
correlations shifted their attitudes remained unchanged, highlighting the need
for further research on whether data visualizations can drive longer-term
changes in views and behavior.
