Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing

People have difficulty understanding statistical information and are unaware of their wrong judgments, particularly in Bayesian reasoning. Psychology studies suggest that the way Bayesian problems are represented can impact comprehension, but few visual designs have been evaluated and only populatio...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics Jg. 18; H. 12; S. 2536 - 2545
Hauptverfasser: Micallef, L., Dragicevic, P., Fekete, J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.12.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
base rate fallacy
Bayes Theorem
Bayesian analysis
Bayesian methods
Bayesian reasoning
Breast cancer
Comprehension
Computer Graphics
Computer Science
Crowdsourcing
Euler diagrams
Female
glyphs
Human-Computer Interaction
Humans
Judgments
Male
Mammography
Populations
probabilistic judgment
Reasoning
Representations
Sociology
Statistical analysis
Texts
Visual
Visual Perception
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
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Zusammenfassung:People have difficulty understanding statistical information and are unaware of their wrong judgments, particularly in Bayesian reasoning. Psychology studies suggest that the way Bayesian problems are represented can impact comprehension, but few visual designs have been evaluated and only populations with a specific background have been involved. In this study, a textual and six visual representations for three classic problems were compared using a diverse subject pool through crowdsourcing. Visualizations included area-proportional Euler diagrams, glyph representations, and hybrid diagrams combining both. Our study failed to replicate previous findings in that subjects' accuracy was remarkably lower and visualizations exhibited no measurable benefit. A second experiment confirmed that simply adding a visualization to a textual Bayesian problem is of little help, even when the text refers to the visualization, but suggests that visualizations are more effective when the text is given without numerical values. We discuss our findings and the need for more such experiments to be carried out on heterogeneous populations of non-experts.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2012.199