Multi-state Visualizations of Descriptive Statistics

Xiaoyi Wang, Kasper Hornbæk

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 2024 International Conference on Advanced Visual Interfaces, AVI 2024
PublisherAssociation for Computing Machinery
Publication date2024
Article number5
ISBN (Electronic)9798400717642
DOIs
Publication statusPublished - 2024
Event2024 International Conference on Advanced Visual Interfaces, AVI 2024 - Arenzano, Genoa, Italy
Duration: 3 Jun 20247 Jun 2024

Conference

Conference2024 International Conference on Advanced Visual Interfaces, AVI 2024
Country/TerritoryItaly
CityArenzano, Genoa
Period03/06/202407/06/2024

Bibliographical note

Publisher Copyright:
© 2024 ACM.

Keywords

  • and Plots
  • Charts
  • Coordinated and Multiple Views
  • Diagrams
  • Human-Subjects Quantitative Studies
  • Uncertainty Visualization

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