{"id":858,"date":"2023-03-20T19:17:35","date_gmt":"2023-03-20T19:17:35","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/interpreting-the-mean-and-median-of-a-dataset-apply-it-2\/"},"modified":"2024-01-31T02:14:38","modified_gmt":"2024-01-31T02:14:38","slug":"interpreting-the-mean-and-median-of-a-dataset-apply-it-2","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/interpreting-the-mean-and-median-of-a-dataset-apply-it-2\/","title":{"raw":"Interpreting the Mean and Median: Apply It 2","rendered":"Interpreting the Mean and Median: Apply It 2"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li>Name the features of the distribution of a data set using statistical language<\/li>\r\n\t<li>Describe the connection between the distribution of a data set and its mean and median<\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2 id=\"IdentMislead\">Misleading Claims<\/h2>\r\n<p>Recall the scenario: A college basketball player is skilled enough to make an NBA roster and is thinking about dropping out of college this year.<\/p>\r\n<p>Let\u2019s explore the distribution of professional basketball salaries in order to better understand the scenario and develop a convincing argument.<\/p>\r\n<p>First, we'll explore the data set and boxplot of NBA salaries[footnote] <em>NBA player salary data set (2017-2018).<\/em> (2018) Kaggle. Retrieved from https:\/\/www.kaggle.com\/koki25ando\/salary [\/footnote] for Texas players in the 2017 \u2013 2018 season.<\/p>\r\n<p>The actual median salary among Texas NBA players was $[latex]1,577,320[\/latex] while the mean salary was $[latex]5,262,279[\/latex].<\/p>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]2075[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]2076[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]2077[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]2078[\/ohm2_question]<\/section>\r\n<p>You've seen that the mean, under certain conditions, can be a misleading indicator of a \"typical\" observation value, such as the salary of a professional basketball player.<\/p>\r\n<p>Now, let's try to apply this understanding to some other types of data collections.<\/p>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li>Name the features of the distribution of a data set using statistical language<\/li>\n<li>Describe the connection between the distribution of a data set and its mean and median<\/li>\n<\/ul>\n<\/section>\n<h2 id=\"IdentMislead\">Misleading Claims<\/h2>\n<p>Recall the scenario: A college basketball player is skilled enough to make an NBA roster and is thinking about dropping out of college this year.<\/p>\n<p>Let\u2019s explore the distribution of professional basketball salaries in order to better understand the scenario and develop a convincing argument.<\/p>\n<p>First, we&#8217;ll explore the data set and boxplot of NBA salaries<a class=\"footnote\" title=\"NBA player salary data set (2017-2018). (2018) Kaggle. Retrieved from https:\/\/www.kaggle.com\/koki25ando\/salary\" id=\"return-footnote-858-1\" href=\"#footnote-858-1\" aria-label=\"Footnote 1\"><sup class=\"footnote\">[1]<\/sup><\/a> for Texas players in the 2017 \u2013 2018 season.<\/p>\n<p>The actual median salary among Texas NBA players was $[latex]1,577,320[\/latex] while the mean salary was $[latex]5,262,279[\/latex].<\/p>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm2075\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=2075&theme=lumen&iframe_resize_id=ohm2075&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm2076\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=2076&theme=lumen&iframe_resize_id=ohm2076&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm2077\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=2077&theme=lumen&iframe_resize_id=ohm2077&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm2078\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=2078&theme=lumen&iframe_resize_id=ohm2078&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<p>You&#8217;ve seen that the mean, under certain conditions, can be a misleading indicator of a &#8220;typical&#8221; observation value, such as the salary of a professional basketball player.<\/p>\n<p>Now, let&#8217;s try to apply this understanding to some other types of data collections.<\/p>\n<hr class=\"before-footnotes clear\" \/><div class=\"footnotes\"><ol><li id=\"footnote-858-1\"> <em>NBA player salary data set (2017-2018).<\/em> (2018) Kaggle. Retrieved from https:\/\/www.kaggle.com\/koki25ando\/salary  <a href=\"#return-footnote-858-1\" class=\"return-footnote\" aria-label=\"Return to footnote 1\">&crarr;<\/a><\/li><\/ol><\/div>","protected":false},"author":13,"menu_order":17,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":834,"module-header":"apply_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\/858"}],"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\/13"}],"version-history":[{"count":4,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/858\/revisions"}],"predecessor-version":[{"id":5308,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/858\/revisions\/5308"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/834"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/858\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=858"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=858"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=858"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=858"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}