Problem
Research questions and friction points this paper is trying to address.
univariate distribution
visualization
chart effectiveness
user preference
task performance
Investigating Performance and Practices with Univariate Distribution Charts
🤖 AI Summary
This study addresses the lack of systematic understanding regarding the effectiveness and usage practices of univariate distribution visualizations across diverse tasks and user groups. Through a mixed-methods approach—combining a click-based selection experiment and survey with 215 participants alongside in-depth interviews with five visualization practitioners—the work systematically evaluates the accuracy, user preferences, and common misinterpretations associated with boxplots, violin plots, jittered scatterplots, and histograms in typical analytical tasks. For the first time, it integrates task performance, subjective preference, and real-world practice, revealing a frequent mismatch between chart familiarity and task accuracy, thereby challenging the assumption that commonly used or conventional visualizations are inherently optimal. The findings demonstrate significant performance differences among chart types in low-level tasks, with widely adopted histograms and boxplots not consistently outperforming alternatives.
📝 Abstract
A range of charts with different strengths and weaknesses exists to support the visual analysis of univariate distributions, with a limited understanding of which charts best support which tasks and users, and how practitioners use charts. We categorize the available charts for univariate distributions into four groups and present the results of a mixed-methods comparison (n=215) of participants' perception and preferences across boxplots, violinplots, jittered stripplots, and histograms as representatives of their respective categories. The click-to-select approach in our study, combined with data on participants' subjective experiences and preferences, allows to both measure accuracy on benchmark tasks and discuss participants' choices qualitatively. Our analysis reveals differences between charts in task accuracy, common misunderstandings, and preferences across various low-level tasks, and indicates that chart preference and familiarity do not necessarily align with participants' task performance. Interviews with five visualization practitioners further reveal that charts widely preferred by general audiences (such as histograms) or commonly used in scientific domains (such as boxplots) are not inherently the most effective for all tasks.
Innovation
Methods, ideas, or system contributions that make the work stand out.
univariate distribution visualization
mixed-methods evaluation
chart preference vs. performance
visual perception study
visualization design guidelines
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