<\/p>\r\n
Understanding these tactics and being vigilant can make you a savvy consumer of data. It empowers you to question the information presented to you, making you less susceptible to manipulation.<\/p>\r\n In the graph below, Microsoft, who was trying to create a conceptual design, created a misleading graphic.<\/p>\r\n <\/p>\r\n The graphic leads consumers to believe Microsoft Edge is [latex]25\\%[\/latex] faster than Chrome and [latex]50\\%[\/latex] faster than Firefox. While Microsoft Edge is slightly faster than Chrome and Firefox, it is not that significantly faster.<\/p>\r\n A column chart would have been a more accurate representation of the data.<\/p>\r\n <\/p>\r\n Now that you’re equipped with the knowledge to scrutinize the finer details of axes and labels, let’s delve into some of the more overt tactics that can make a graph misleading.<\/p>\n There are various ways a graph can be misleading. For instance, the scale might be manipulated to exaggerate differences, or data points might be selectively omitted to support a particular narrative. Understanding these tactics is the first step in becoming a savvy consumer of data.<\/p>\n <\/p>\n Understanding these tactics and being vigilant can make you a savvy consumer of data. It empowers you to question the information presented to you, making you less susceptible to manipulation.<\/p>\n In the graph below, Microsoft, who was trying to create a conceptual design, created a misleading graphic.<\/p>\n <\/p>\n The graphic leads consumers to believe Microsoft Edge is [latex]25\\%[\/latex] faster than Chrome and [latex]50\\%[\/latex] faster than Firefox. While Microsoft Edge is slightly faster than Chrome and Firefox, it is not that significantly faster.<\/p>\n A column chart would have been a more accurate representation of the data.<\/p>\n <\/p>\n![\"A](\"https:\/\/content-cdn.one.lumenlearning.com\/wp-content\/uploads\/sites\/18\/2023\/10\/27151907\/misleading-graphs-19.png\")
<\/center>\r\n![\"An](\"https:\/\/content-cdn.one.lumenlearning.com\/wp-content\/uploads\/sites\/18\/2023\/10\/27153051\/Screenshot-2023-10-27-113001.png\")
<\/center><\/section>","rendered":"Identifying Misleading and Erroneous Graphical Displays Cont.<\/h2>\n
Types of Misleading Graphs<\/h3>\n
\n\n
\n Type<\/th>\n Definition<\/th>\n Example<\/th>\n Real-World Connection<\/th>\n Pro Tip<\/th>\n<\/tr>\n \n Manipulated Scale<\/strong><\/td>\n Changing the scale on the axes to exaggerate differences.<\/td>\n A line graph might show a stock price rising dramatically, but upon closer inspection, the [latex]y[\/latex]-axis starts at [latex]$99[\/latex] and ends at [latex]$101[\/latex], making a [latex]$2[\/latex] difference look enormous.<\/td>\n Often used in financial news to create hype around a stock or make economic changes seem more significant.<\/td>\n Always check the scale of the graph.<\/td>\n<\/tr>\n \n Selective Omission of Data<\/strong><\/td>\n Showing only specific data points to paint a biased picture.<\/td>\n A graph might show only the positive outcomes of a particular treatment painting an overly optimistic picture.<\/td>\n Common in pharmaceutical ads or political campaigns where only favorable data is presented.<\/td>\n Be wary of graphs that seem too good to be true.<\/td>\n<\/tr>\n \n Cherry-Picking Time Frames<\/strong><\/td>\n Using specific time frames to create a misleading impression of performance.<\/td>\n A graph highlights the performance of an investment fund over its best quarter.<\/td>\n Often in investment brochures or sports stats to show a player’s or fund’s peak performance.<\/td>\n Always consider the time frame.<\/td>\n<\/tr>\n \n Misleading Visual Elements<\/strong><\/td>\n Using visual tricks to emphasize points that aren’t necessarily significant.<\/td>\n A 3D pie chart uses bright colors and disproportionate sizes to mislead or emphasize a point that might not be significant when looked at objectively.<\/td>\n Used in marketing campaigns to make products seem more appealing than they are.<\/td>\n Look beyond the visual gimmicks.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n <\/div>\n<\/div>\n<\/section>\n","protected":false},"author":15,"menu_order":13,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":88,"module-header":"learn_it","content_attributions":[],"internal_book_links":[],"video_content":null,"cc_video_embed_content":{"cc_scripts":"","media_targets":[]},"try_it_collection":null,"_links":{"self":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/10035"}],"collection":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/users\/15"}],"version-history":[{"count":22,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/10035\/revisions"}],"predecessor-version":[{"id":14285,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/10035\/revisions\/14285"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/parts\/88"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/10035\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/media?parent=10035"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=10035"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/contributor?post=10035"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/license?post=10035"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}