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Multi-state Visualizations of Descriptive Statistics

Published: 03 June 2024 Publication History

Abstract

Decision making often requires a series of low-level tasks involving descriptive statistics, each of them supported by a single visual representation, such as a bar chart or a violin plot. However, a single representation conveys limited information and little work has investigated using a combination of visual representations to facilitate the decision-making process. We propose multi-state visualizations, which allow users to switch easily between different visual representations (or states). Such visualizations provide users with more opportunities to explore and interpret the underlying data. We present three candidate multi-state visualizations, pairing error bars with violin plots, quantile dot plots, or hypothetical outcome plots. In a crowd-sourced study, we compare multi-state and single-state visualizations to investigate if they enhance users’ accuracy and confidence in making probability estimates. The results show that participants using multi-state visualization feel more confident and make more accurate estimations. Furthermore, we discuss the benefits of using multiple states in visualization for uncertainty visualizations.

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Appendices for Multi-state Visualizations of Descriptive Statistics

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cover image ACM Other conferences
AVI '24: Proceedings of the 2024 International Conference on Advanced Visual Interfaces
June 2024
578 pages
ISBN:9798400717642
DOI:10.1145/3656650
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Association for Computing Machinery

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Published: 03 June 2024

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Author Tags

  1. Charts
  2. Coordinated and Multiple Views
  3. Diagrams
  4. Human-Subjects Quantitative Studies
  5. Uncertainty Visualization
  6. and Plots

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  • Research-article
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  • Refereed limited

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Appendices for Multi-state Visualizations of Descriptive Statistics https://dl.acm.org/doi/10.1145/3656650.3656662#Appendices_MSV.pdf

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  • Innovation Fund Denmark

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AVI 2024

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AVI '24 Paper Acceptance Rate 21 of 82 submissions, 26%;
Overall Acceptance Rate 128 of 490 submissions, 26%

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