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|a 10.1109/TVCG.2022.3210763
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Newburger, Eric
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Fitting Bell Curves to Data Distributions Using Visualization
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 15.11.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Idealized probability distributions, such as normal or other curves, lie at the root of confirmatory statistical tests. But how well do people understand these idealized curves? In practical terms, does the human visual system allow us to match sample data distributions with hypothesized population distributions from which those samples might have been drawn? And how do different visualization techniques impact this capability? This article shares the results of a crowdsourced experiment that tested the ability of respondents to fit normal curves to four different data distribution visualizations: bar histograms, dotplot histograms, strip plots, and boxplots. We find that the crowd can estimate the center (mean) of a distribution with some success and little bias. We also find that people generally overestimate the standard deviation-which we dub the "umbrella effect" because people tend to want to cover the whole distribution using the curve, as if sheltering it from the heavens above-and that strip plots yield the best accuracy
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Correll, Michael
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Elmqvist, Niklas
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 29(2023), 12 vom: 01. Dez., Seite 5372-5383
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|g year:2023
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|u http://dx.doi.org/10.1109/TVCG.2022.3210763
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