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

TL;DR

This study investigates how prior attitudes influence the persuasiveness of scatterplot visualizations, especially regarding statistical correlations and uncertainty, revealing that strong prior beliefs can limit belief change despite visual evidence.

Contribution

It provides new insights into how prior attitudes affect the persuasive impact of data visualizations and the role of uncertainty representations in this process.

Findings

Strong prior attitudes reduce belief change when data contradicts views.

Visual uncertainty representations may amplify resistance to belief change.

Belief shifts do not necessarily lead to attitude or behavior change.

Abstract

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.