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.

翻译：数据可视化对于公共卫生、气候变化和社会经济政策等关键议题的科学传播至关重要。它们的设计不仅旨在提供信息，更意在说服人们做出重大决策（例如接种疫苗）。当受众已有与数据相悖的信念和态度时，此类可视化是否仍具说服力？本文通过实验，考察在呈现不同统计不确定性表征的散点图时，现有态度（如对COVID-19疫苗接种的正面或负面态度）如何影响人们对统计相关性信念的变化。研究发现，当展示与既有观点相矛盾的数据时，强烈的先验态度与较小的信念变化相关，且视觉不确定性表征可能放大这一效应。最后，即使参与者对相关性的信念发生转变，其态度仍保持不变，这凸显了需要进一步研究数据可视化能否驱动观点与行为的长期改变。