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问题的科学交流至关重要。数据可视化通常不仅是为了提供信息,而且是为了说服人们做出相应的决定(例如接种疫苗 ) 。这种可视化是否具有说服力,特别是当受众的信仰和态度与数据相矛盾时?在本文中,我们研究了现有态度(例如对COVID-19疫苗的正面或负面态度)对统计相关性观念变化的影响,当人们看到分布式可视化与统计不确定性的不同表现时。我们发现,在提出与现有观点相矛盾的数据时,以往的强烈态度与较小的信仰变化有关,而视觉不确定性的表达可能扩大这一影响。 最后,即使参与者对相关性的信念改变了他们的态度,也有必要进一步研究数据可视化是否会推动观点和行为的长期变化。