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Cited by 122Data Visualization and AnalyticsVisual and Cognitive Learning ProcessesStatistics Education and Methodologies

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data

Kale, Alex, Nguyen, Francis, Kay, Matthew, Hullman, Jessica·2019·IEEE Transactions on Visualization and Computer Graphics
DOI 10.1109/tvcg.2018.2864909
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Abstract

Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.

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Research works printed but not linked: 7

  1. irwin, 2014, How Not toBe Misled by the Jobs Report, The New York Times
  2. aisch, 2016, Live Presidential Forecast
  3. quealy, 2015, The first g.o.p.debate: Who's in, who's out and the role of chance, The New York Times
  4. bui, 2017, What the tax bill would look like for 25,000 middle-class families, The New York Times
  5. franconeri, 0, Personal communication
  6. bostock, 2014, Who Will Win The Senate?, The New York Times
  7. kay, 2016, When (ish) is my bus? user-centered visualizations of uncertainty in everyday, mobile predictive systems, Proc CHI2016

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Metadata

Authors
Kale, A., Nguyen, F., Kay, M., & Hullman, J.
Affiliations
  • University of Washington (US)
  • University of Michigan (US)
  • Northwestern University (US)
ORCID
Venue
IEEE Transactions on Visualization and Computer Graphics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-01
Volume
25
Issue
1
Pages
892-902
ISSN
  • 1077-2626 (ISSN-L)
  • 1941-0506
  • 2160-9306
DOI
10.1109/tvcg.2018.2864909
OpenAlex
W2888554701
Cited by
122
References
67
Citations by year
2026: 8 · 2025: 11 · 2024: 10 · 2023: 14 · 2022: 20 · 2021: 27 · 2020: 19 · 2019: 12
FWCI
7.38
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • IEEE Transactions on Visualization and Computer Graphics (publishedVersion, public-domain)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
  • Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
Keywords
uncertainty communication, perceptual decision making, error bars, sampling error, news visualization, visualization evaluation

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