{"id":41,"date":"2023-01-31T00:45:58","date_gmt":"2023-01-31T00:45:58","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/applications-of-bar-graphs-learn-it-2\/"},"modified":"2025-05-11T19:32:05","modified_gmt":"2025-05-11T19:32:05","slug":"applications-of-bar-graphs-learn-it-1","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/applications-of-bar-graphs-learn-it-1\/","title":{"raw":"Applications of Bar Graphs: Learn It 1","rendered":"Applications of Bar Graphs: Learn It 1"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li>Create side-by-side and stacked bar graphs using technology<\/li>\r\n\t<li>Use side-by-side and stacked bar graphs to compare different groups<\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2>Comparing a Variable Across Groups<\/h2>\r\n<p>Both pie charts and bar graphs are good visual representations of a categorical variable from a single population or group. But what can we do if we want to compare a categorical variable across multiple groups?<\/p>\r\n<section class=\"textbox keyTakeaway\">\r\n<h3>contingency table (two-way table)<\/h3>\r\n<p>A <strong>contingency table<\/strong> is a table that displays the results of two categorical variables simultaneously. This is also called a <strong>two-way table<\/strong>.<\/p>\r\n<ul>\r\n\t<li>The advantage of a contingency table is that you can see each precise percentage of responses or frequency of responses.<\/li>\r\n\t<li>A disadvantage is that the table does not present a strong visual comparison between the groups.<\/li>\r\n<\/ul>\r\n<\/section>\r\n<h3>Side-by-Side Bar Charts<\/h3>\r\n<p><strong>Side-by-side bar charts<\/strong>\u00a0and\u00a0<strong>stacked bar charts<\/strong> are extensions of bar graphs or pie charts that allow us to conduct comparisons between multiple data sets. These bar charts will help us to explore how to display and interpret changes in a categorical variable of interest when comparing multiple populations or groups of interest.<\/p>\r\n<section class=\"textbox keyTakeaway\">\r\n<h3>side-by-side bar charts<\/h3>\r\n<p>Side-by-side bar charts present data for two categorical variables from more than one group by creating multiple bars on the chart for each group, with one bar for each variable.<\/p>\r\n<\/section>\r\n<section class=\"textbox example\">The 2016 presidential race was very different from the one in 2020. In 2016, fewer people turned out to vote,[footnote]Schaul, K., Rabinowitz, K., &amp; Mellnik, T. (2020, December 28). <em>2020 turnout is the highest in over a century<\/em>. The Washington Post. <a href=\"https:\/\/www.washingtonpost.com\/graphics\/2020\/elections\/voter-turnout\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.washingtonpost.com\/graphics\/2020\/elections\/voter-turnout\/<\/a>[\/footnote] more people were deemed ineligible ([latex]6[\/latex] million felons in 2016[footnote]Uggen, C., Larson, R., &amp; Shannon, S. (2016, October 16). <em>6 million lost voters: State-level estimates of felony disenfranchisement, 2016<\/em>. The Sentencing Project. <a href=\"https:\/\/www.sentencingproject.org\/publications\/6-million-lost-voters-state-level-estimates-felony-disenfranchisement-2016\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.sentencingproject.org\/publications\/6-million-lost-voters-state-level-estimates-felony-disenfranchisement-2016\/<\/a>[\/footnote] compared to [latex]5.1[\/latex] million felons in 2020),[footnote]Maxouris, C. (2020, October 15). <em>More than 5 million people with felony convictions can\u2019t vote in this year\u2019s election, advocacy group finds<\/em>. CNN. <a href=\"https:\/\/www.cnn.com\/2020\/10\/15\/us\/felony-convictions-voting-sentencing-project-study\/index.html\" target=\"_blank\" rel=\"noopener\">https:\/\/www.cnn.com\/2020\/10\/15\/us\/felony-convictions-voting-sentencing-project-study\/index.html<\/a>[\/footnote] and the election results were much closer. In 2016, Hillary Clinton won the popular vote, and fewer than [latex]80,000[\/latex] votes out of [latex]137[\/latex] million votes cast determined the outcome of Donald Trump being selected as our president.[footnote]<em>Why voting matters: Supreme Court edition<\/em>. (2018, June 28). Axios. Retrieved from <a href=\"https:\/\/www.axios.com\/hillary-clinton-2016-election-votes-supreme-court-liberal-justice-1b4bc4fc-9fad-44b4-ab54-9ef86aa9c1f1.html\" target=\"_blank\" rel=\"noopener\">https:\/\/www.axios.com\/hillary-clinton-2016-election-votes-supreme-court-liberal-justice-1b4bc4fc-9fad-44b4-ab54-9ef86aa9c1f1.html<\/a>[\/footnote]Looking to our future, one question might be, \u201cIf we increase legitimate voter participation, will one party benefit?\u201d We can better answer this question if we study the voting patterns of different groups within the United States. CNN used an exit poll to estimate the presidential 2020 voting patterns by race.[footnote]<em>Exit polls<\/em>. (2020). CNN Politics. Retrieved from <a href=\"https:\/\/www.cnn.com\/election\/2020\/exit-polls\/president\/national-results\" target=\"_blank\" rel=\"noopener\">https:\/\/www.cnn.com\/election\/2020\/exit-polls\/president\/national-results<\/a>[\/footnote] The following is a table of the results, where the rows describe the different groups of people of interest (White, Black, Latinx, Asian, and Other) and the columns represent the vote choices (Biden, Trump, or Other).\r\n\r\n<table style=\"border-collapse: collapse; width: 100%; height: 84px;\" border=\"1\">\r\n<tbody>\r\n<tr>\r\n<td style=\"width: 99.8639%; text-align: center;\" colspan=\"4\"><strong>Presidential 2020 Voting Patterns Percentage by Race<\/strong><\/td>\r\n<\/tr>\r\n<tr style=\"height: 14px;\">\r\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\">\u00a0<\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\"><strong>Biden<\/strong><\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\"><strong>Trump<\/strong><\/td>\r\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\"><strong>Other<\/strong><\/td>\r\n<\/tr>\r\n<tr style=\"height: 14px;\">\r\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>White<\/strong><\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]41[\/latex]<\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]58[\/latex]<\/td>\r\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]1[\/latex]<\/td>\r\n<\/tr>\r\n<tr style=\"height: 14px;\">\r\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Black<\/strong><\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]87[\/latex]<\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]12[\/latex]<\/td>\r\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]1[\/latex]<\/td>\r\n<\/tr>\r\n<tr style=\"height: 14px;\">\r\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Latinx<\/strong><\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]65[\/latex]<\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]32[\/latex]<\/td>\r\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]3[\/latex]<\/td>\r\n<\/tr>\r\n<tr style=\"height: 14px;\">\r\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Asian<\/strong><\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]61[\/latex]<\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]34[\/latex]<\/td>\r\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]5[\/latex]<\/td>\r\n<\/tr>\r\n<tr style=\"height: 14px;\">\r\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Other<\/strong><\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]55[\/latex]<\/td>\r\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]41[\/latex]<\/td>\r\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]4[\/latex]<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<p>Among Asians, for example, [latex]61[\/latex]% voted for Biden, [latex]34[\/latex]% voted for Trump, and the remaining [latex]5[\/latex]% voted for someone else.<\/p>\r\n<p>Translating the table to a visual might aid in the comparison between the groups.<\/p>\r\n<p><span style=\"font-size: 1rem; font-weight: normal; text-align: initial;\">Let's take the table of voting patterns we looked at above and compare it to a side-by-side bar graph containing the same information.<\/span><\/p>\r\n\r\n[caption id=\"attachment_848\" align=\"aligncenter\" width=\"1066\"]<img class=\"wp-image-848 size-full\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/5826\/2022\/09\/07174417\/Picture11.png\" alt=\"Bar graph demonstrating how America voted in 2020 based off of different racial groups.\" width=\"1066\" height=\"404\" \/> Figure 1. This graph allows for quick visual comparisons of voter preferences across racial groups, highlighting differences in support for Biden, Trump, and others.[\/caption]\r\n\r\n<p>The groups of interest are listed on the horizontal axis (Whites, Blacks, Latinx, Asian, and Other), and the percentages associated with each voter choice are on the vertical axis.<\/p>\r\n<\/section>\r\n<section class=\"textbox proTip\">When percentages of an entire group are reported, within each group, the heights of the bars should total [latex]100[\/latex]. This represents [latex]100\\%[\/latex] of all responses within that group. Using a side-by-side bar graph that chooses to represent percentages within groups (as opposed to the numbers of actual ballots cast within groups) means that you cannot make conclusions about counts. Rather, you can make conclusions about relative proportions or percentages within each group.<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]759[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]760[\/ohm2_question]<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li>Create side-by-side and stacked bar graphs using technology<\/li>\n<li>Use side-by-side and stacked bar graphs to compare different groups<\/li>\n<\/ul>\n<\/section>\n<h2>Comparing a Variable Across Groups<\/h2>\n<p>Both pie charts and bar graphs are good visual representations of a categorical variable from a single population or group. But what can we do if we want to compare a categorical variable across multiple groups?<\/p>\n<section class=\"textbox keyTakeaway\">\n<h3>contingency table (two-way table)<\/h3>\n<p>A <strong>contingency table<\/strong> is a table that displays the results of two categorical variables simultaneously. This is also called a <strong>two-way table<\/strong>.<\/p>\n<ul>\n<li>The advantage of a contingency table is that you can see each precise percentage of responses or frequency of responses.<\/li>\n<li>A disadvantage is that the table does not present a strong visual comparison between the groups.<\/li>\n<\/ul>\n<\/section>\n<h3>Side-by-Side Bar Charts<\/h3>\n<p><strong>Side-by-side bar charts<\/strong>\u00a0and\u00a0<strong>stacked bar charts<\/strong> are extensions of bar graphs or pie charts that allow us to conduct comparisons between multiple data sets. These bar charts will help us to explore how to display and interpret changes in a categorical variable of interest when comparing multiple populations or groups of interest.<\/p>\n<section class=\"textbox keyTakeaway\">\n<h3>side-by-side bar charts<\/h3>\n<p>Side-by-side bar charts present data for two categorical variables from more than one group by creating multiple bars on the chart for each group, with one bar for each variable.<\/p>\n<\/section>\n<section class=\"textbox example\">The 2016 presidential race was very different from the one in 2020. In 2016, fewer people turned out to vote,<a class=\"footnote\" title=\"Schaul, K., Rabinowitz, K., &amp; Mellnik, T. (2020, December 28). 2020 turnout is the highest in over a century. The Washington Post. https:\/\/www.washingtonpost.com\/graphics\/2020\/elections\/voter-turnout\/\" id=\"return-footnote-41-1\" href=\"#footnote-41-1\" aria-label=\"Footnote 1\"><sup class=\"footnote\">[1]<\/sup><\/a> more people were deemed ineligible ([latex]6[\/latex] million felons in 2016<a class=\"footnote\" title=\"Uggen, C., Larson, R., &amp; Shannon, S. (2016, October 16). 6 million lost voters: State-level estimates of felony disenfranchisement, 2016. The Sentencing Project. https:\/\/www.sentencingproject.org\/publications\/6-million-lost-voters-state-level-estimates-felony-disenfranchisement-2016\/\" id=\"return-footnote-41-2\" href=\"#footnote-41-2\" aria-label=\"Footnote 2\"><sup class=\"footnote\">[2]<\/sup><\/a> compared to [latex]5.1[\/latex] million felons in 2020),<a class=\"footnote\" title=\"Maxouris, C. (2020, October 15). More than 5 million people with felony convictions can\u2019t vote in this year\u2019s election, advocacy group finds. CNN. https:\/\/www.cnn.com\/2020\/10\/15\/us\/felony-convictions-voting-sentencing-project-study\/index.html\" id=\"return-footnote-41-3\" href=\"#footnote-41-3\" aria-label=\"Footnote 3\"><sup class=\"footnote\">[3]<\/sup><\/a> and the election results were much closer. In 2016, Hillary Clinton won the popular vote, and fewer than [latex]80,000[\/latex] votes out of [latex]137[\/latex] million votes cast determined the outcome of Donald Trump being selected as our president.<a class=\"footnote\" title=\"Why voting matters: Supreme Court edition. (2018, June 28). Axios. Retrieved from https:\/\/www.axios.com\/hillary-clinton-2016-election-votes-supreme-court-liberal-justice-1b4bc4fc-9fad-44b4-ab54-9ef86aa9c1f1.html\" id=\"return-footnote-41-4\" href=\"#footnote-41-4\" aria-label=\"Footnote 4\"><sup class=\"footnote\">[4]<\/sup><\/a>Looking to our future, one question might be, \u201cIf we increase legitimate voter participation, will one party benefit?\u201d We can better answer this question if we study the voting patterns of different groups within the United States. CNN used an exit poll to estimate the presidential 2020 voting patterns by race.<a class=\"footnote\" title=\"Exit polls. (2020). CNN Politics. Retrieved from https:\/\/www.cnn.com\/election\/2020\/exit-polls\/president\/national-results\" id=\"return-footnote-41-5\" href=\"#footnote-41-5\" aria-label=\"Footnote 5\"><sup class=\"footnote\">[5]<\/sup><\/a> The following is a table of the results, where the rows describe the different groups of people of interest (White, Black, Latinx, Asian, and Other) and the columns represent the vote choices (Biden, Trump, or Other).<\/p>\n<table style=\"border-collapse: collapse; width: 100%; height: 84px;\">\n<tbody>\n<tr>\n<td style=\"width: 99.8639%; text-align: center;\" colspan=\"4\"><strong>Presidential 2020 Voting Patterns Percentage by Race<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 14px;\">\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\">\u00a0<\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\"><strong>Biden<\/strong><\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\"><strong>Trump<\/strong><\/td>\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\"><strong>Other<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 14px;\">\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>White<\/strong><\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]41[\/latex]<\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]58[\/latex]<\/td>\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]1[\/latex]<\/td>\n<\/tr>\n<tr style=\"height: 14px;\">\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Black<\/strong><\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]87[\/latex]<\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]12[\/latex]<\/td>\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]1[\/latex]<\/td>\n<\/tr>\n<tr style=\"height: 14px;\">\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Latinx<\/strong><\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]65[\/latex]<\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]32[\/latex]<\/td>\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]3[\/latex]<\/td>\n<\/tr>\n<tr style=\"height: 14px;\">\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Asian<\/strong><\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]61[\/latex]<\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]34[\/latex]<\/td>\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]5[\/latex]<\/td>\n<\/tr>\n<tr style=\"height: 14px;\">\n<td style=\"width: 21.7687%; height: 14px; text-align: center;\"><strong>Other<\/strong><\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]55[\/latex]<\/td>\n<td style=\"width: 26.2585%; height: 14px; text-align: center;\">[latex]41[\/latex]<\/td>\n<td style=\"width: 25.5782%; height: 14px; text-align: center;\">[latex]4[\/latex]<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Among Asians, for example, [latex]61[\/latex]% voted for Biden, [latex]34[\/latex]% voted for Trump, and the remaining [latex]5[\/latex]% voted for someone else.<\/p>\n<p>Translating the table to a visual might aid in the comparison between the groups.<\/p>\n<p><span style=\"font-size: 1rem; font-weight: normal; text-align: initial;\">Let&#8217;s take the table of voting patterns we looked at above and compare it to a side-by-side bar graph containing the same information.<\/span><\/p>\n<figure id=\"attachment_848\" aria-describedby=\"caption-attachment-848\" style=\"width: 1066px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-848 size-full\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/5826\/2022\/09\/07174417\/Picture11.png\" alt=\"Bar graph demonstrating how America voted in 2020 based off of different racial groups.\" width=\"1066\" height=\"404\" \/><figcaption id=\"caption-attachment-848\" class=\"wp-caption-text\">Figure 1. This graph allows for quick visual comparisons of voter preferences across racial groups, highlighting differences in support for Biden, Trump, and others.<\/figcaption><\/figure>\n<p>The groups of interest are listed on the horizontal axis (Whites, Blacks, Latinx, Asian, and Other), and the percentages associated with each voter choice are on the vertical axis.<\/p>\n<\/section>\n<section class=\"textbox proTip\">When percentages of an entire group are reported, within each group, the heights of the bars should total [latex]100[\/latex]. This represents [latex]100\\%[\/latex] of all responses within that group. Using a side-by-side bar graph that chooses to represent percentages within groups (as opposed to the numbers of actual ballots cast within groups) means that you cannot make conclusions about counts. Rather, you can make conclusions about relative proportions or percentages within each group.<\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm759\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=759&theme=lumen&iframe_resize_id=ohm759&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm760\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=760&theme=lumen&iframe_resize_id=ohm760&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<hr class=\"before-footnotes clear\" \/><div class=\"footnotes\"><ol><li id=\"footnote-41-1\">Schaul, K., Rabinowitz, K., &amp; Mellnik, T. (2020, December 28). <em>2020 turnout is the highest in over a century<\/em>. The Washington Post. <a href=\"https:\/\/www.washingtonpost.com\/graphics\/2020\/elections\/voter-turnout\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.washingtonpost.com\/graphics\/2020\/elections\/voter-turnout\/<\/a> <a href=\"#return-footnote-41-1\" class=\"return-footnote\" aria-label=\"Return to footnote 1\">&crarr;<\/a><\/li><li id=\"footnote-41-2\">Uggen, C., Larson, R., &amp; Shannon, S. (2016, October 16). <em>6 million lost voters: State-level estimates of felony disenfranchisement, 2016<\/em>. The Sentencing Project. <a href=\"https:\/\/www.sentencingproject.org\/publications\/6-million-lost-voters-state-level-estimates-felony-disenfranchisement-2016\/\" target=\"_blank\" rel=\"noopener\">https:\/\/www.sentencingproject.org\/publications\/6-million-lost-voters-state-level-estimates-felony-disenfranchisement-2016\/<\/a> <a href=\"#return-footnote-41-2\" class=\"return-footnote\" aria-label=\"Return to footnote 2\">&crarr;<\/a><\/li><li id=\"footnote-41-3\">Maxouris, C. (2020, October 15). <em>More than 5 million people with felony convictions can\u2019t vote in this year\u2019s election, advocacy group finds<\/em>. CNN. <a href=\"https:\/\/www.cnn.com\/2020\/10\/15\/us\/felony-convictions-voting-sentencing-project-study\/index.html\" target=\"_blank\" rel=\"noopener\">https:\/\/www.cnn.com\/2020\/10\/15\/us\/felony-convictions-voting-sentencing-project-study\/index.html<\/a> <a href=\"#return-footnote-41-3\" class=\"return-footnote\" aria-label=\"Return to footnote 3\">&crarr;<\/a><\/li><li id=\"footnote-41-4\"><em>Why voting matters: Supreme Court edition<\/em>. (2018, June 28). Axios. Retrieved from <a href=\"https:\/\/www.axios.com\/hillary-clinton-2016-election-votes-supreme-court-liberal-justice-1b4bc4fc-9fad-44b4-ab54-9ef86aa9c1f1.html\" target=\"_blank\" rel=\"noopener\">https:\/\/www.axios.com\/hillary-clinton-2016-election-votes-supreme-court-liberal-justice-1b4bc4fc-9fad-44b4-ab54-9ef86aa9c1f1.html<\/a> <a href=\"#return-footnote-41-4\" class=\"return-footnote\" aria-label=\"Return to footnote 4\">&crarr;<\/a><\/li><li id=\"footnote-41-5\"><em>Exit polls<\/em>. (2020). CNN Politics. Retrieved from <a href=\"https:\/\/www.cnn.com\/election\/2020\/exit-polls\/president\/national-results\" target=\"_blank\" rel=\"noopener\">https:\/\/www.cnn.com\/election\/2020\/exit-polls\/president\/national-results<\/a> <a href=\"#return-footnote-41-5\" class=\"return-footnote\" aria-label=\"Return to footnote 5\">&crarr;<\/a><\/li><\/ol><\/div>","protected":false},"author":6,"menu_order":11,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":20,"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\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/41"}],"collection":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/users\/6"}],"version-history":[{"count":21,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/41\/revisions"}],"predecessor-version":[{"id":6598,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/41\/revisions\/6598"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/41\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=41"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=41"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=41"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=41"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}