{"id":446,"date":"2024-10-18T01:29:06","date_gmt":"2024-10-18T01:29:06","guid":{"rendered":"https:\/\/pressbooks.ccconline.org\/mat1260\/?post_type=chapter&#038;p=446"},"modified":"2025-06-12T17:56:06","modified_gmt":"2025-06-12T17:56:06","slug":"chapter-10-inference-for-two-populations","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ccconline.org\/mat1260\/chapter\/chapter-10-inference-for-two-populations\/","title":{"raw":"Chapter 10: Inference for Two Populations","rendered":"Chapter 10: Inference for Two Populations"},"content":{"raw":"<p id=\"fs-idp16810352\">When conducting a hypothesis test that compares two independent population proportions, the following characteristics should be present:<\/p>\r\n\r\n<ol id=\"element-381\">\r\n \t<li>The two independent samples are simple random samples that are independent.<\/li>\r\n \t<li>The number of successes is at least five, and the number of failures is at least five, for each of the samples.<\/li>\r\n \t<li>Growing literature states that the population must be at least ten or 20 times the size of the sample. This keeps each population from being over-sampled and causing incorrect results.<\/li>\r\n<\/ol>\r\n<p id=\"element-3812\">Comparing two proportions, like comparing two means, is common. If two estimated proportions are different, it may be due to a difference in the populations or it may be due to chance. A hypothesis test can help determine if a difference in the estimated proportions reflects a difference in the population proportions.<\/p>\r\n<p id=\"para-00001\">Like the case of differences in sample means, we construct a sampling distribution for differences in sample proportions:<\/p>\r\n[latex]p'A=\\frac{X_{A}}{n_{A}}[\/latex] and [latex]p'\\frac{X_{B}}{X_{B}}[\/latex] are the sample proportions for the two sets of data in question [latex]X_{A}[\/latex] and [latex]X_{B}[\/latex].\r\n<p id=\"fs-idm58849136\">The difference of two proportions follows an approximate normal distribution. Generally, the null hypothesis states that the two proportions are the same. That is, <strong><em data-effect=\"italics\">H<sub>0<\/sub><\/em>: <em data-effect=\"italics\">p<sub>A<\/sub><\/em> = <em data-effect=\"italics\">p<sub>B<\/sub><\/em><\/strong>. To conduct the test, we use a <span id=\"term-00001\" data-type=\"term\">pooled proportion<\/span>, <strong><em data-effect=\"italics\">p<sub>c<\/sub><\/em><\/strong>.<\/p>\r\n\r\n<div id=\"element-845\" class=\"unnumbered\" data-type=\"equation\" data-label=\"\">\r\n<div data-type=\"title\">The pooled proportion is calculated as follows:<\/div>\r\n[latex]p_{c}=\\frac{x_{A}+x_{B}}{n_{A}+n_{B}}[\/latex]\r\n\r\n&nbsp;\r\n<div id=\"element-630\" class=\"unnumbered\" data-type=\"equation\" data-label=\"\">\r\n<div data-type=\"title\">The distribution for the differences is:<\/div>\r\n<div data-type=\"title\">[latex]P'_{A}-P'_{B\\sim}N[0,\\sqrt{p_{c}(1-p_{c})(\\frac{1}{n_{A}}+\\frac{1}{n_{B}})}][\/latex]<\/div>\r\n<div data-type=\"title\"><\/div>\r\n<div id=\"element-225\" class=\"unnumbered\" data-type=\"equation\" data-label=\"\">\r\n<div data-type=\"title\">The test statistic (<em data-effect=\"italics\">z<\/em>-score) is:<\/div>\r\n[latex] z=\\frac{(p'_{A}-p'_{B})-(p_{a}-p_{b})}{\\sqrt{P_{C}(1-P_{C})(\\frac{1}{n_{a}}+\\frac{1}{n_{B}})}}[\/latex]\r\n\r\n&nbsp;\r\n<div><\/div>\r\n<div><span id=\"output\" class=\"attribution-output\"><a href=\"https:\/\/openstax.org\/books\/introductory-statistics-2e\/pages\/10-3-comparing-two-independent-population-proportions\" target=\"_blank\" rel=\"noopener\">\"OpenStax Introductory Statistics\"<\/a> by <a>Barbara Illowsky, Susan Dean<\/a> is licensed under <a href=\"http:\/\/creativecommons.org\/licenses\/by\/4.0\" target=\"_blank\" rel=\"noopener\">CC BY 4.0<\/a><\/span><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>","rendered":"<p id=\"fs-idp16810352\">When conducting a hypothesis test that compares two independent population proportions, the following characteristics should be present:<\/p>\n<ol id=\"element-381\">\n<li>The two independent samples are simple random samples that are independent.<\/li>\n<li>The number of successes is at least five, and the number of failures is at least five, for each of the samples.<\/li>\n<li>Growing literature states that the population must be at least ten or 20 times the size of the sample. This keeps each population from being over-sampled and causing incorrect results.<\/li>\n<\/ol>\n<p id=\"element-3812\">Comparing two proportions, like comparing two means, is common. If two estimated proportions are different, it may be due to a difference in the populations or it may be due to chance. A hypothesis test can help determine if a difference in the estimated proportions reflects a difference in the population proportions.<\/p>\n<p id=\"para-00001\">Like the case of differences in sample means, we construct a sampling distribution for differences in sample proportions:<\/p>\n<p>[latex]p'A=\\frac{X_{A}}{n_{A}}[\/latex] and [latex]p'\\frac{X_{B}}{X_{B}}[\/latex] are the sample proportions for the two sets of data in question [latex]X_{A}[\/latex] and [latex]X_{B}[\/latex].<\/p>\n<p id=\"fs-idm58849136\">The difference of two proportions follows an approximate normal distribution. Generally, the null hypothesis states that the two proportions are the same. That is, <strong><em data-effect=\"italics\">H<sub>0<\/sub><\/em>: <em data-effect=\"italics\">p<sub>A<\/sub><\/em> = <em data-effect=\"italics\">p<sub>B<\/sub><\/em><\/strong>. To conduct the test, we use a <span id=\"term-00001\" data-type=\"term\">pooled proportion<\/span>, <strong><em data-effect=\"italics\">p<sub>c<\/sub><\/em><\/strong>.<\/p>\n<div id=\"element-845\" class=\"unnumbered\" data-type=\"equation\" data-label=\"\">\n<div data-type=\"title\">The pooled proportion is calculated as follows:<\/div>\n<p>[latex]p_{c}=\\frac{x_{A}+x_{B}}{n_{A}+n_{B}}[\/latex]<\/p>\n<p>&nbsp;<\/p>\n<div id=\"element-630\" class=\"unnumbered\" data-type=\"equation\" data-label=\"\">\n<div data-type=\"title\">The distribution for the differences is:<\/div>\n<div data-type=\"title\">[latex]P'_{A}-P'_{B\\sim}N[0,\\sqrt{p_{c}(1-p_{c})(\\frac{1}{n_{A}}+\\frac{1}{n_{B}})}][\/latex]<\/div>\n<div data-type=\"title\"><\/div>\n<div id=\"element-225\" class=\"unnumbered\" data-type=\"equation\" data-label=\"\">\n<div data-type=\"title\">The test statistic (<em data-effect=\"italics\">z<\/em>-score) is:<\/div>\n<p>[latex]z=\\frac{(p'_{A}-p'_{B})-(p_{a}-p_{b})}{\\sqrt{P_{C}(1-P_{C})(\\frac{1}{n_{a}}+\\frac{1}{n_{B}})}}[\/latex]<\/p>\n<p>&nbsp;<\/p>\n<div><\/div>\n<div><span id=\"output\" class=\"attribution-output\"><a href=\"https:\/\/openstax.org\/books\/introductory-statistics-2e\/pages\/10-3-comparing-two-independent-population-proportions\" target=\"_blank\" rel=\"noopener\">&#8220;OpenStax Introductory Statistics&#8221;<\/a> by <a>Barbara Illowsky, Susan Dean<\/a> is licensed under <a href=\"http:\/\/creativecommons.org\/licenses\/by\/4.0\" target=\"_blank\" rel=\"noopener\">CC BY 4.0<\/a><\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"author":150,"menu_order":12,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-446","chapter","type-chapter","status-publish","hentry"],"part":421,"_links":{"self":[{"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/pressbooks\/v2\/chapters\/446","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/wp\/v2\/users\/150"}],"version-history":[{"count":6,"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/pressbooks\/v2\/chapters\/446\/revisions"}],"predecessor-version":[{"id":1175,"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/pressbooks\/v2\/chapters\/446\/revisions\/1175"}],"part":[{"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/pressbooks\/v2\/parts\/421"}],"metadata":[{"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/pressbooks\/v2\/chapters\/446\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/wp\/v2\/media?parent=446"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/pressbooks\/v2\/chapter-type?post=446"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/wp\/v2\/contributor?post=446"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.ccconline.org\/mat1260\/wp-json\/wp\/v2\/license?post=446"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}