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Data visualizations are vital to scientific communication on critical issuessuch as public health, climate change, and socioeconomic policy. They are oftendesigned not just to inform, but to persuade people to make consequentialdecisions (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 ornegative attitudes toward COVID-19 vaccination) on changes in beliefs aboutstatistical correlations when viewing scatterplot visualizations with differentrepresentations of statistical uncertainty. We find that strong prior attitudesare associated with smaller belief changes when presented with data thatcontradicts existing views, and that visual uncertainty representations mayamplify this effect. Finally, even when participants' beliefs aboutcorrelations shifted their attitudes remained unchanged, highlighting the needfor further research on whether data visualizations can drive longer-termchanges in views and behavior.

When do data visualizations persuade? The impact of prior attitudes on learning about correlations from scatterplot visualizations