Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences about Reliability of Variable Ordering
Many visual depictions of probability distributions, such as error bars, are difficult for users to accurately interpret. We present and study an alternative representation, Hypothetical Outcome Plots (HOPs), that animates a finite set of individual draws. In contrast to the statistical background r...
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| Veröffentlicht in: | PloS one 2015-11, Vol.10 (11), p.e0142444-e0142444 |
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| description | Many visual depictions of probability distributions, such as error bars, are difficult for users to accurately interpret. We present and study an alternative representation, Hypothetical Outcome Plots (HOPs), that animates a finite set of individual draws. In contrast to the statistical background required to interpret many static representations of distributions, HOPs require relatively little background knowledge to interpret. Instead, HOPs enables viewers to infer properties of the distribution using mental processes like counting and integration. We conducted an experiment comparing HOPs to error bars and violin plots. With HOPs, people made much more accurate judgments about plots of two and three quantities. Accuracy was similar with all three representations for most questions about distributions of a single quantity. |
| doi_str_mv | 10.1371/journal.pone.0142444 |
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We present and study an alternative representation, Hypothetical Outcome Plots (HOPs), that animates a finite set of individual draws. In contrast to the statistical background required to interpret many static representations of distributions, HOPs require relatively little background knowledge to interpret. Instead, HOPs enables viewers to infer properties of the distribution using mental processes like counting and integration. We conducted an experiment comparing HOPs to error bars and violin plots. With HOPs, people made much more accurate judgments about plots of two and three quantities. Accuracy was similar with all three representations for most questions about distributions of a single quantity.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0142444</identifier><identifier>PMID: 26571487</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Bars ; Clinical trials ; Computer graphics ; Computer simulation ; Confidence intervals ; Data Interpretation, Statistical ; Discrimination (Psychology) ; Errors ; Experiments ; Hops ; Humans ; Judgments ; Medical care ; Medical research ; Models, Statistical ; Normal Distribution ; Probability ; Probability distribution ; Quality management ; Rain ; Random variables ; Representations ; Reproducibility of Results ; Standard deviation ; Statistical analysis ; United States</subject><ispartof>PloS one, 2015-11, Vol.10 (11), p.e0142444-e0142444</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Hullman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Hullman et al 2015 Hullman et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-b13c858e2e7f1c4c77da1b0309820687d33f236adf18abbe4e9a955bdc4d1e263</citedby><cites>FETCH-LOGICAL-c692t-b13c858e2e7f1c4c77da1b0309820687d33f236adf18abbe4e9a955bdc4d1e263</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4646698/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4646698/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26571487$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Papaleo, Elena</contributor><creatorcontrib>Hullman, Jessica</creatorcontrib><creatorcontrib>Resnick, Paul</creatorcontrib><creatorcontrib>Adar, Eytan</creatorcontrib><title>Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences about Reliability of Variable Ordering</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Many visual depictions of probability distributions, such as error bars, are difficult for users to accurately interpret. We present and study an alternative representation, Hypothetical Outcome Plots (HOPs), that animates a finite set of individual draws. In contrast to the statistical background required to interpret many static representations of distributions, HOPs require relatively little background knowledge to interpret. Instead, HOPs enables viewers to infer properties of the distribution using mental processes like counting and integration. We conducted an experiment comparing HOPs to error bars and violin plots. With HOPs, people made much more accurate judgments about plots of two and three quantities. 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| subjects | Analysis Bars Clinical trials Computer graphics Computer simulation Confidence intervals Data Interpretation, Statistical Discrimination (Psychology) Errors Experiments Hops Humans Judgments Medical care Medical research Models, Statistical Normal Distribution Probability Probability distribution Quality management Rain Random variables Representations Reproducibility of Results Standard deviation Statistical analysis United States |
| title | Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences about Reliability of Variable Ordering |
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