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It proposes defensive raincloud plot designs that ensure good mutual coverage for surfacing features of interest and reducing missed or overemphasized trivial aspects.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/presentations/","Presentations",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/teru-teru-bzu-defensive-raincloud-plots/565305/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/teru-teru-bzu-defensive-raincloud-plots/565305.png","ImageObject",442,249,{"name":42,"@type":43},"hdkawk","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-24","2026-10-13",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why can single univariate visualizations be insufficient for distribution analysis?","Question",{"text":63,"@type":64},"Single summaries like histograms, box plots, or density plots may fail to surface important features, miss outliers or multimodality, or be misinterpreted, limiting their usefulness for all analysis tasks.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What is the core idea behind raincloud plots in this paper?",{"text":68,"@type":64},"Raincloud plots combine a “cloud” for overall distribution shape with “rain” for individual data points, optionally adding “lightning” marks for additional statistics such as medians or inferential results.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the paper evaluate and improve raincloud plot designs?",{"text":72,"@type":64},"It examines the design space through simulation studies, focusing on robustness and the ability to reliably surface distributional features even under potentially adversarial conditions, then proposes best practices and defensive design classes.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},565305,1792359660,{"code":4,"msg":82,"data":83},"success",[84,88,93,98,103,108,113,118,123],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":86,"slug":87},11,90,"presentations",{"id":89,"doc_module":22,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},12,"Resumes",80,"resumes",{"id":94,"doc_module":22,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},14,"Invoices",70,"invoices",{"id":99,"doc_module":22,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},15,"Posters",60,"posters",{"id":104,"doc_module":22,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},16,"Social Media",50,"social-media",{"id":109,"doc_module":22,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},17,"Forms",40,"forms",{"id":114,"doc_module":22,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},18,"Letters",30,"letters",{"id":119,"doc_module":22,"doc_module_name":25,"category_name":120,"show_sort_weight":121,"slug":122},21,"Paper Templates",5,"papers-templates",{"id":124,"doc_module":22,"doc_module_name":25,"category_name":125,"show_sort_weight":4,"slug":126},158,"General","general-158",{"code":4,"msg":82,"data":128},{"doc_id":79,"user_id":129,"nickname":42,"user_avatar":130,"doc_module":22,"category_id":85,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":89,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":140,"read_time":33},3985747859855,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Eurographics Conference on Visualization (EuroVis) 2023 D. Archambault, R. Bujack, and T. Schreck (Guest Editors)  \nCOMPUTER GRAPHICS forum Volume 42 (2023), Number 3  \nTeru Teru Bzu: Defensive Raincloud Plots  \narXiv :2303 . 17709v1 [ cs .HC] 30 Mar 2023  \nMichael Correll 1  \n1 Work completed while at Tableau Research  \n(a) Density-Jittered-Boxplot (b) Boxplot-Strip-Mean Marker (c) Histogram-Dot Plot-Mean+Interval  \nFigure 1: Three examples of raincloud plots showing the distribution of the yearly number of days with precipitation in Seattle, 1948-2017 . A raincloud plot is made of three component visualizations: clouds that provide the overall shape of the distribution, rain that plot individual data values, and optional lightning for additional derived or inferential statistics.  \nAbstract  \nUnivariate visualizations like histograms, rug plots, or box plots provide concise visual summaries of distributions. However, each individual visualization may fail to robustly distinguish important features of a distribution, or provide sufficient informa tion for all of the relevant tasks involved in summarizing univariate data. One solution is to juxtapose or superimpose multiple univariate visualizations in the same chart, as in Allen et al.'s [APW􀀃 19] “raincloud plots.” In this paper I examine the design space of raincloud plots, and, through a series of simulation studies, explore designs where the component visualizations  \nmutually “defend” against situations where important distribution features are missed or trivial features are given undue promi nence. I suggest a class of “defensive” raincloud plot designs that provide good mutual coverage for surfacing distributional features of interest.  \nCCS Concepts  \n• Human-centered computing ! Visualization systems and tools; Visual analytics; Visualization techniques;  \n1. Introduction  \nProminent examples like Anscombe's quartet [Ans73] or the Datasaurus dozen [MF17] show that concise sets of summary statistics may fail to identify distributional features of interest. Visualizations can augment these numerical summaries and provide more context or detail. Yet, simple visualizations like box plots or error bars can similarly fail to surface important distributional features [CM05, PHKD06], or, worse, be misinterpreted [CG14] . Even more complex univariate visualizations like density plots and histograms can fail to reliably surface outliers, missing data, or multimodality [CLKS19] .  \nThe deficiencies of individual classes of univariate visualizationsto support the many, occasionally conflicting tasks involved in univariate distributions have led to a growing number of examples of  \nplots that juxtapose or superimpose multiple univariate charts together, where each design is intended to support different sets of tasks relating to the data distribution. The terminology for this class of design varies, for example “hybrid plots” [BDL􀀃 20, PKRJ10],“ensemble plots” [CLKS19], or “RDI plots” (standing for “Raw (data), Description and Inference”) [Phi17] . For this paper, I anchor on a specific class of such designs: raincloud plots [APW􀀃 19]  \n(Figure 1) . As with teru teru bo¯zu, the Japanese good luck charms for warding off rain [Sto06], these rain cloud plots ward off situations where viewers might miss critical information about a data distribution, and promise a sunny day of visual analytics based on trusted and well-understood data.  \nRaincloud plots juxtapose a “cloud” that provides a summary of the overall shape or extent of the distribution (such as a density plot or violin plot) with “rain” that plots individual data points (such as  \n© 2023 Eurographics-The European Association for Computer Graphics and John Wiley & Sons Ltd.  \nM. Correll / Designing Defensive Raincloud Plots  \na jittered dot plot or strip plot) . The clouds and rain can be augmented with additional marks indicating medians, normal modes, or inferential statistics [PKRJ10] . I was unable to identify an appropriat","cbCaibXUf1E4nmkL","https://ap.wps.com/l/cbCaibXUf1E4nmkL","pdf",541283,"English","# Abstract\n# 1. Introduction\n## Raincloud plots and defensiveness\n## Design space and simulation studies","[{\"question\":\"Why can single univariate visualizations be insufficient for distribution analysis?\",\"answer\":\"Single summaries like histograms, box plots, or density plots may fail to surface important features, miss outliers or multimodality, or be misinterpreted, limiting their usefulness for all analysis tasks.\"},{\"question\":\"What is the core idea behind raincloud plots in this paper?\",\"answer\":\"Raincloud plots combine a “cloud” for overall distribution shape with “rain” for individual data points, optionally adding “lightning” marks for additional statistics such as medians or inferential results.\"},{\"question\":\"How does the paper evaluate and improve raincloud plot designs?\",\"answer\":\"It examines the design space through simulation studies, focusing on robustness and the ability to reliably surface distributional features even under potentially adversarial conditions, then proposes best practices and defensive design classes.\"}]","Teru Teru Bzu - Defensive Raincloud Plots | PDF",1791850975]