{"id":1273,"date":"2017-03-08T08:55:13","date_gmt":"2017-03-08T08:55:13","guid":{"rendered":"https:\/\/blogs.scu.edu\/dataviz\/?p=1273"},"modified":"2017-03-08T08:55:13","modified_gmt":"2017-03-08T08:55:13","slug":"how-visualization-fool-you","status":"publish","type":"post","link":"https:\/\/blogs.scu.edu\/dataviz\/2017\/03\/08\/how-visualization-fool-you\/","title":{"rendered":"How Visualization Fool You"},"content":{"rendered":"<p style=\"text-align: center\"><em><strong>Abstract:<\/strong>\u00a0Evolutionary pressure has made us visual beings.\u00a0Because we respond so strongly to visual cues, charts and graphs have the power to move us in a way that other ways of presenting data can\u2019t match. Therefore data visualization as one of the most important tools we have to analyze data can be misleading as well. In this blog post we\u2019ll take a look at 3 of the most common ways in which visualizations can be misleading.<\/em><\/p>\n<p style=\"text-align: left\">Charts can mislead us into believing things that aren\u2019t true. Sometimes this is accidental, but other times we are being deliberately manipulated. Sometimes it\u2019s easy to spot what\u2019s wrong, but other times the sleight of hand is very subtle.<\/p>\n<p style=\"text-align: left\"><b>Dodgy Diagrams:\u00a0<\/b>The most notorious of the data visualization deceiver\u2019s tricks is to use chart axes that don\u2019t start at zero. We\u2019re very good at comparing the lengths of objects, so choosing a non-zero axis can greatly magnify small or meaningless differences.\u00a0Taken to an extreme, this technique can make differences in data seem much larger than they are.<\/p>\n<p style=\"text-align: left\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-1274\" src=\"https:\/\/blogs.scu.edu\/dataviz\/files\/2017\/03\/misleading1_fox-300x224.jpg\" alt=\"misleading1_fox\" width=\"300\" height=\"224\" srcset=\"https:\/\/blogs.scu.edu\/dataviz\/files\/2017\/03\/misleading1_fox-300x224.jpg 300w, https:\/\/blogs.scu.edu\/dataviz\/files\/2017\/03\/misleading1_fox.jpg 500w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Cumulative Graphs:<\/strong>\u00a0Many people opt to create cumulative graphs of things like number of users, revenue, downloads, or other important metrics.<\/p>\n<p><strong>Ignoring Conventions:\u00a0<\/strong>One of the most insidious tactics people use in constructing misleading data visualizations is to violate standard practices. We\u2019re used to the fact that pie charts represent parts of a whole or that timelines progress from left to right. So when those rules get violated, we have a difficult time seeing what\u2019s actually going on. We\u2019re wired to misinterpret the data, due to our reliance on these conventions.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-1275\" src=\"https:\/\/blogs.scu.edu\/dataviz\/files\/2017\/03\/misleading3_deaths-240x300.jpg\" alt=\"misleading3_deaths\" width=\"350\" height=\"438\" srcset=\"https:\/\/blogs.scu.edu\/dataviz\/files\/2017\/03\/misleading3_deaths-240x300.jpg 240w, https:\/\/blogs.scu.edu\/dataviz\/files\/2017\/03\/misleading3_deaths.jpg 604w\" sizes=\"auto, (max-width: 350px) 100vw, 350px\" \/><\/p>\n<p><strong>Conclusion:\u00a0<\/strong>Here are some simple rules we should use to keep our work\u00a0virtuous.<\/p>\n<ul>\n<ul>\n<li>Always start your plots from zero, unless doing so would be misleading.<\/li>\n<li>Use a linear axis scale \u2013 avoid different sized categories and log plots unless there are good reasons to do otherwise.<\/li>\n<li>Never, ever forget that correlation is not causation. No matter how tempting it is, don\u2019t do it. Bear in mind that your audience will almost certainly see correlation as equaling causation, so be careful.<\/li>\n<li>Maps are beautiful, but they can be powerfully misleading. Never use them alone and always consider the unintended message you might be transmitting.<\/li>\n<\/ul>\n<\/ul>\n<p>References:<\/p>\n<p>http:\/\/data-informed.com\/whats-wrong-picture-art-honest-visualizations\/<\/p>\n<p>http:\/\/www.cs.tufts.edu\/comp\/250VIS\/papers\/chi2015-deception.pdf<\/p>\n<p>http:\/\/avoinelama.fi\/hingo\/kirjoituksia\/misleadingvisualizations.html<\/p>\n<p>http:\/\/www.citylab.com\/design\/2015\/06\/when-maps-lie\/396761\/<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract:\u00a0Evolutionary pressure has made us visual beings.\u00a0Because we respond so strongly to visual cues, charts and graphs have the power to move us in a way that other ways of presenting data can\u2019t match. Therefore data visualization as one of the most important tools we have to analyze data can be misleading as well. In &hellip; <a href=\"https:\/\/blogs.scu.edu\/dataviz\/2017\/03\/08\/how-visualization-fool-you\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">How Visualization Fool You<\/span><\/a><\/p>\n","protected":false},"author":1688,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"qubely_global_settings":"","qubely_interactions":"","kk_blocks_editor_width":"","_kiokenblocks_attr":"","_kiokenblocks_dimensions":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1273","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"gutentor_comment":0,"qubely_featured_image_url":null,"qubely_author":{"display_name":"hamed","author_link":"https:\/\/blogs.scu.edu\/dataviz\/author\/hamed\/"},"qubely_comment":0,"qubely_category":"<a href=\"https:\/\/blogs.scu.edu\/dataviz\/category\/uncategorized\/\" rel=\"category tag\">Uncategorized<\/a>","qubely_excerpt":"Abstract:\u00a0Evolutionary pressure has made us visual beings.\u00a0Because we respond so strongly to visual cues, charts and graphs have the power to move us in a way that other ways of presenting data can\u2019t match. Therefore data visualization as one of the most important tools we have to analyze data can be misleading as well. In&hellip;","post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/1273","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/users\/1688"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/comments?post=1273"}],"version-history":[{"count":1,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/1273\/revisions"}],"predecessor-version":[{"id":1277,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/1273\/revisions\/1277"}],"wp:attachment":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/media?parent=1273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/categories?post=1273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/tags?post=1273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}