{"id":1366,"date":"2023-06-22T02:20:32","date_gmt":"2023-06-22T02:20:32","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/conditions-for-anova-fresh-take\/"},"modified":"2025-05-16T22:54:37","modified_gmt":"2025-05-16T22:54:37","slug":"conditions-for-anova-fresh-take","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/conditions-for-anova-fresh-take\/","title":{"raw":"Conditions for ANOVA - Fresh Take","rendered":"Conditions for ANOVA &#8211; Fresh Take"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Check the conditions for a one-way ANOVA hypothesis test&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:12801,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0,&quot;15&quot;:&quot;arial&quot;,&quot;16&quot;:10}\">Check the conditions for a one-way ANOVA hypothesis test<\/span><\/li>\r\n<\/ul>\r\n<\/section>\r\n<p>The purpose of a one-way ANOVA test is to determine the existence of a statistically significant difference among several group means. The test actually uses\u00a0<strong>variances<\/strong>\u00a0to help determine if the means are equal or not.<\/p>\r\n<section class=\"textbox keyTakeaway\">\r\n<h3>conditions for one-way ANOVA<\/h3>\r\n<p>In order to perform a one-way ANOVA test, there are four basic <strong>assumptions<\/strong>\u00a0to be fulfilled:<\/p>\r\n<ol>\r\n\t<li>Data for ANOVA\r\n\r\n<ul>\r\n\t<li>The factor is a categorical variable.<\/li>\r\n\t<li>The response is a numerical variable.<\/li>\r\n\t<li>The mean of the response variable is the parameter of interest.<\/li>\r\n<\/ul>\r\n<\/li>\r\n\t<li>All samples are randomly selected and independent.<\/li>\r\n\t<li>The populations are assumed to have\u00a0equal standard deviations (or variances).<\/li>\r\n\t<li>An ANOVA also requires that the data within each group be normally distributed, but testing for that is outside the scope of this course.<\/li>\r\n<\/ol>\r\n<\/section>\r\n<section class=\"textbox watchIt\" aria-label=\"Watch It\">\r\n<p>[embed]https:\/\/youtu.be\/HGFiWMA5OC8[\/embed]<\/p>\r\n<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Check the conditions for a one-way ANOVA hypothesis test&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:12801,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0,&quot;15&quot;:&quot;arial&quot;,&quot;16&quot;:10}\">Check the conditions for a one-way ANOVA hypothesis test<\/span><\/li>\n<\/ul>\n<\/section>\n<p>The purpose of a one-way ANOVA test is to determine the existence of a statistically significant difference among several group means. The test actually uses\u00a0<strong>variances<\/strong>\u00a0to help determine if the means are equal or not.<\/p>\n<section class=\"textbox keyTakeaway\">\n<h3>conditions for one-way ANOVA<\/h3>\n<p>In order to perform a one-way ANOVA test, there are four basic <strong>assumptions<\/strong>\u00a0to be fulfilled:<\/p>\n<ol>\n<li>Data for ANOVA\n<ul>\n<li>The factor is a categorical variable.<\/li>\n<li>The response is a numerical variable.<\/li>\n<li>The mean of the response variable is the parameter of interest.<\/li>\n<\/ul>\n<\/li>\n<li>All samples are randomly selected and independent.<\/li>\n<li>The populations are assumed to have\u00a0equal standard deviations (or variances).<\/li>\n<li>An ANOVA also requires that the data within each group be normally distributed, but testing for that is outside the scope of this course.<\/li>\n<\/ol>\n<\/section>\n<section class=\"textbox watchIt\" aria-label=\"Watch It\">\n<p><iframe loading=\"lazy\" id=\"oembed-1\" title=\"5 5B Conditions for ANOVA\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/HGFiWMA5OC8?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<\/section>\n","protected":false},"author":8,"menu_order":20,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":1348,"module-header":"fresh_take","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\/1366"}],"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\/8"}],"version-history":[{"count":3,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/1366\/revisions"}],"predecessor-version":[{"id":6843,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/1366\/revisions\/6843"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/1348"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/1366\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=1366"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=1366"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=1366"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=1366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}