{"id":72,"date":"2023-01-31T00:46:23","date_gmt":"2023-01-31T00:46:23","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/comparing-quantitative-distributions-learn-it-3\/"},"modified":"2025-05-11T19:44:35","modified_gmt":"2025-05-11T19:44:35","slug":"comparing-quantitative-distributions-learn-it-3","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/comparing-quantitative-distributions-learn-it-3\/","title":{"raw":"Comparing Quantitative Distributions: Learn It 3","rendered":"Comparing Quantitative Distributions: Learn It 3"},"content":{"raw":"<section class=\"textbox learningGoals focusable\" tabindex=\"-1\">\r\n<ul>\r\n\t<li>Compare data sets by describing their shapes, centers, spreads, and outliers<\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2>Which one should I use?<\/h2>\r\n<p>We used two types of graphs to analyze the distribution of a quantitative variable: histograms and dotplots.<\/p>\r\n<ul>\r\n\t<li>Some observations about\u00a0<strong>histograms<\/strong><em>:<\/em>\r\n<ul>\r\n\t<li>Individual variable values are not visible.<\/li>\r\n\t<li>Grouping individuals into bins of equal-sized intervals is particularly useful when analyzing large data sets.<\/li>\r\n\t<li>We can easily use percentages, also called relative frequencies, to describe the distribution.<\/li>\r\n\t<li>Descriptions of shape, center, and spread are affected by how the bins are defined.<\/li>\r\n\t<li>Histograms can make it easier to identify skewness and modality (i.e., whether the distribution is symmetric, skewed, unimodal, bimodal, etc.), particularly for large data sets.<\/li>\r\n\t<li>Outliers may not be as apparent in histograms, especially if the bin width is too large, which can mask the variability in the data.<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<ul>\r\n\t<li>Some observations about\u00a0<strong>dotplots<\/strong><em>:<\/em>\r\n<ul>\r\n\t<li>Individual variable values are visible, particularly when the data set is small.<\/li>\r\n\t<li>Descriptions of shape, center, and spread are not affected by how the dotplot is constructed.<\/li>\r\n\t<li>We can accurately calculate the overall range (largest value \u2013 smallest value).<\/li>\r\n\t<li>Dotplots can make it easier to identify outliers and gaps within the data due to the visibility of individual data points.<\/li>\r\n\t<li>Dotplots are most effective for smaller datasets, as they can become cluttered and less informative with larger datasets.<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<section class=\"textbox proTip\">\r\n<p>How do we decide when to use a dotplot and when to use a histogram? <br \/>\r\n<strong>There are no rules here.<\/strong> Each type of graph can highlight different aspects of the data.<\/p>\r\n<ul>\r\n\t<li><strong>Size of the Dataset:<\/strong> The choice between a dotplot and a histogram often depends on the size of the dataset. Dotplots work well for small to moderately sized datasets, while histograms are better suited for larger datasets.<\/li>\r\n\t<li><strong>Detail Required:<\/strong> Dotplots provide detail at the individual data point level, which can be useful for in-depth analysis, whereas histograms provide a summary view, which can be useful for identifying general patterns.<\/li>\r\n<\/ul>\r\n<\/section>\r\n<section class=\"textbox tryIt\">\r\n<p>[ohm2_question hide_question_numbers=1]17013[\/ohm2_question]<\/p>\r\n<\/section>","rendered":"<section class=\"textbox learningGoals focusable\" tabindex=\"-1\">\n<ul>\n<li>Compare data sets by describing their shapes, centers, spreads, and outliers<\/li>\n<\/ul>\n<\/section>\n<h2>Which one should I use?<\/h2>\n<p>We used two types of graphs to analyze the distribution of a quantitative variable: histograms and dotplots.<\/p>\n<ul>\n<li>Some observations about\u00a0<strong>histograms<\/strong><em>:<\/em>\n<ul>\n<li>Individual variable values are not visible.<\/li>\n<li>Grouping individuals into bins of equal-sized intervals is particularly useful when analyzing large data sets.<\/li>\n<li>We can easily use percentages, also called relative frequencies, to describe the distribution.<\/li>\n<li>Descriptions of shape, center, and spread are affected by how the bins are defined.<\/li>\n<li>Histograms can make it easier to identify skewness and modality (i.e., whether the distribution is symmetric, skewed, unimodal, bimodal, etc.), particularly for large data sets.<\/li>\n<li>Outliers may not be as apparent in histograms, especially if the bin width is too large, which can mask the variability in the data.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ul>\n<li>Some observations about\u00a0<strong>dotplots<\/strong><em>:<\/em>\n<ul>\n<li>Individual variable values are visible, particularly when the data set is small.<\/li>\n<li>Descriptions of shape, center, and spread are not affected by how the dotplot is constructed.<\/li>\n<li>We can accurately calculate the overall range (largest value \u2013 smallest value).<\/li>\n<li>Dotplots can make it easier to identify outliers and gaps within the data due to the visibility of individual data points.<\/li>\n<li>Dotplots are most effective for smaller datasets, as they can become cluttered and less informative with larger datasets.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<section class=\"textbox proTip\">\n<p>How do we decide when to use a dotplot and when to use a histogram? <br \/>\n<strong>There are no rules here.<\/strong> Each type of graph can highlight different aspects of the data.<\/p>\n<ul>\n<li><strong>Size of the Dataset:<\/strong> The choice between a dotplot and a histogram often depends on the size of the dataset. Dotplots work well for small to moderately sized datasets, while histograms are better suited for larger datasets.<\/li>\n<li><strong>Detail Required:<\/strong> Dotplots provide detail at the individual data point level, which can be useful for in-depth analysis, whereas histograms provide a summary view, which can be useful for identifying general patterns.<\/li>\n<\/ul>\n<\/section>\n<section class=\"textbox tryIt\">\n<iframe loading=\"lazy\" id=\"ohm17013\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=17013&theme=lumen&iframe_resize_id=ohm17013&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><br \/>\n<\/section>\n","protected":false},"author":6,"menu_order":28,"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\/72"}],"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":14,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/72\/revisions"}],"predecessor-version":[{"id":6617,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/72\/revisions\/6617"}],"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\/72\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=72"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=72"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=72"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=72"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}