{"id":806,"date":"2017-02-13T07:15:10","date_gmt":"2017-02-13T07:15:10","guid":{"rendered":"https:\/\/blogs.scu.edu\/dataviz\/?p=806"},"modified":"2017-02-13T07:15:10","modified_gmt":"2017-02-13T07:15:10","slug":"spot-visualization-lies-part-ii","status":"publish","type":"post","link":"https:\/\/blogs.scu.edu\/dataviz\/2017\/02\/13\/spot-visualization-lies-part-ii\/","title":{"rendered":"Spot Visualization Lies &#8211; Part II"},"content":{"rendered":"<p><strong>Odd Choice of Binning<\/strong><\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/i0.wp.com\/flowingdata.com\/wp-content\/uploads\/2017\/02\/Binning.png?resize=768%2C226\" alt=\"\" width=\"768\" height=\"226\" \/><\/strong><\/p>\n<p>Instead of showing the full range of variation in a data set, someone might try to oversimplify a complex pattern. It&#8217;s easy to transform a continuous variable into a categorical one. Broad binning can be useful, but complexity is often what makes things worth looking at. Be aware of oversimplification.<\/p>\n<p><strong>Area Sized by Single Dimension<\/strong><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/i0.wp.com\/flowingdata.com\/wp-content\/uploads\/2017\/02\/Area-scaled-linearly.png?resize=768%2C465\" alt=\"\" width=\"768\" height=\"465\" \/><\/p>\n<p>Most of time human&#8217;s eyes can\u00a0not accurately tell how much is a square or a circle. When data are linearly sized an area-based encoding, like a square or a circle, they might be sniffing for dramatics.<\/p>\n<p><strong>Variation with Area Dimensions<\/strong><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/i2.wp.com\/flowingdata.com\/wp-content\/uploads\/2017\/02\/Area-dimensions.png?resize=768%2C284\" alt=\"\" width=\"768\" height=\"284\" \/><\/p>\n<p>Maybe someone knows how area as a visual encoding works, and then they go and do something like the above. Theses fill the same amount of area, but they look very different and still dramatic.<\/p>\n<p><strong>Extra Dimensions<\/strong><\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/i2.wp.com\/flowingdata.com\/wp-content\/uploads\/2017\/02\/Extra-dimension.png?resize=768%2C316\" alt=\"\" width=\"768\" height=\"316\" \/><\/strong><\/p>\n<p>When you see a three dimensional chart that is three dimensions for no good reason. It is worth to question the data, the chart, the author and everything based on the chart. That extra dimension could be nothing but just a distract factor.<\/p>\n<p><strong>Important: It does not absolutely mean a visualization is lying just because it exhibit one of the previously mentioned qualities. With that in mind, make sure you have the right reaction before you call someone a liar.<\/strong><\/p>\n<p><strong>As rule of thumb, scrutinize charts that shock or seem more dramatic than you thought.\u00a0<\/strong><\/p>\n<p><a href=\"https:\/\/flowingdata.com\/2017\/02\/09\/how-to-spot-visualization-lies\/\">https:\/\/flowingdata.com\/2017\/02\/09\/how-to-spot-visualization-lies\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Odd Choice of Binning Instead of showing the full range of variation in a data set, someone might try to oversimplify a complex pattern. It&#8217;s easy to transform a continuous variable into a categorical one. Broad binning can be useful, but complexity is often what makes things worth looking at. Be aware of oversimplification. Area &hellip; <a href=\"https:\/\/blogs.scu.edu\/dataviz\/2017\/02\/13\/spot-visualization-lies-part-ii\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Spot Visualization Lies &#8211; Part II<\/span><\/a><\/p>\n","protected":false},"author":1849,"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-806","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"gutentor_comment":0,"qubely_featured_image_url":null,"qubely_author":{"display_name":"dixu","author_link":"https:\/\/blogs.scu.edu\/dataviz\/author\/dixu\/"},"qubely_comment":0,"qubely_category":"<a href=\"https:\/\/blogs.scu.edu\/dataviz\/category\/uncategorized\/\" rel=\"category tag\">Uncategorized<\/a>","qubely_excerpt":"Odd Choice of Binning Instead of showing the full range of variation in a data set, someone might try to oversimplify a complex pattern. It&#8217;s easy to transform a continuous variable into a categorical one. Broad binning can be useful, but complexity is often what makes things worth looking at. Be aware of oversimplification. Area&hellip;","post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/806","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\/1849"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/comments?post=806"}],"version-history":[{"count":1,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/806\/revisions"}],"predecessor-version":[{"id":810,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/806\/revisions\/810"}],"wp:attachment":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/media?parent=806"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/categories?post=806"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/tags?post=806"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}