{"id":757,"date":"2023-03-08T17:54:04","date_gmt":"2023-03-08T17:54:04","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/experimental-design-dig-deeper\/"},"modified":"2025-05-11T18:46:19","modified_gmt":"2025-05-11T18:46:19","slug":"experimental-design-fresh-take","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/experimental-design-fresh-take\/","title":{"raw":"Experimental Design: Fresh Take","rendered":"Experimental Design: Fresh Take"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li>Identify the key components of experimental design<\/li>\r\n\t<li>Use experimental design to create a hypothetical experiment to answer a research question<\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2>Treatment, Control, Random Assignment, and Replication<\/h2>\r\n<section class=\"textbox recall\" aria-label=\"Recall\">\r\n<h3><strong>The Main Idea<\/strong><\/h3>\r\n<p>In an experiment, a researcher wants to know if a <strong>factor of interest<\/strong> (the <strong>explanatory variable<\/strong>) has an effect on a<strong> response variable<\/strong>.<\/p>\r\n<p>The researcher\u00a0<strong>randomly assigns<\/strong> experimental units into either a <strong>treatment group<\/strong>\u00a0(in which the explanatory variable is manipulated) or a control group (in which a placebo is applied, or the units are subjected to otherwise normal conditions).<\/p>\r\n<p><strong>Replication<\/strong> occurs by repeating the same treatment or control conditions with many individuals.<\/p>\r\n<\/section>\r\n<p>The following video puts all these ideas together.<\/p>\r\n<section class=\"textbox watchIt\">\r\n<p>[embed]https:\/\/www.youtube.com\/embed\/DaBq0naj0YY[\/embed]<\/p>\r\n<\/section>\r\n<h2>Confounding and Nuisance Factors<\/h2>\r\n<p>You may have heard the phrase, \"correlation does not imply causation.\" Just because two variables are associated by some correlation, doesn't necessarily mean that one has caused the effect in the other. We must be careful not to rush to a conclusion if in fact confounding variables are leading an observational study to draw unusual conclusions.<\/p>\r\n<section class=\"textbox recall\">\r\n<h3><strong>The Main Idea<\/strong><\/h3>\r\n<p><strong>Confounding Factors<\/strong> are variables that aren't accounted for in the design of a study but that may influence other variables in the study. They are associated with both the explanatory and response variables.<\/p>\r\n<p><strong>Nuisance Factors<\/strong> are variables known to the researcher that could influence other variables in the study. The researcher has purposefully kept these variables the same across all levels or treatments to control them.<\/p>\r\n<\/section>\r\n<section class=\"textbox watchIt\" aria-label=\"Watch It\">\r\n<p>This video will help you understand confounding factors.<\/p>\r\n<p>[embed]https:\/\/www.youtube.com\/embed\/fjdb4ID7HVg[\/embed]<\/p>\r\n<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li>Identify the key components of experimental design<\/li>\n<li>Use experimental design to create a hypothetical experiment to answer a research question<\/li>\n<\/ul>\n<\/section>\n<h2>Treatment, Control, Random Assignment, and Replication<\/h2>\n<section class=\"textbox recall\" aria-label=\"Recall\">\n<h3><strong>The Main Idea<\/strong><\/h3>\n<p>In an experiment, a researcher wants to know if a <strong>factor of interest<\/strong> (the <strong>explanatory variable<\/strong>) has an effect on a<strong> response variable<\/strong>.<\/p>\n<p>The researcher\u00a0<strong>randomly assigns<\/strong> experimental units into either a <strong>treatment group<\/strong>\u00a0(in which the explanatory variable is manipulated) or a control group (in which a placebo is applied, or the units are subjected to otherwise normal conditions).<\/p>\n<p><strong>Replication<\/strong> occurs by repeating the same treatment or control conditions with many individuals.<\/p>\n<\/section>\n<p>The following video puts all these ideas together.<\/p>\n<section class=\"textbox watchIt\">\n<p><iframe loading=\"lazy\" id=\"oembed-1\" title=\"Introduction to experiment design | Study design | AP Statistics | Khan Academy\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/DaBq0naj0YY?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<\/section>\n<h2>Confounding and Nuisance Factors<\/h2>\n<p>You may have heard the phrase, &#8220;correlation does not imply causation.&#8221; Just because two variables are associated by some correlation, doesn&#8217;t necessarily mean that one has caused the effect in the other. We must be careful not to rush to a conclusion if in fact confounding variables are leading an observational study to draw unusual conclusions.<\/p>\n<section class=\"textbox recall\">\n<h3><strong>The Main Idea<\/strong><\/h3>\n<p><strong>Confounding Factors<\/strong> are variables that aren&#8217;t accounted for in the design of a study but that may influence other variables in the study. They are associated with both the explanatory and response variables.<\/p>\n<p><strong>Nuisance Factors<\/strong> are variables known to the researcher that could influence other variables in the study. The researcher has purposefully kept these variables the same across all levels or treatments to control them.<\/p>\n<\/section>\n<section class=\"textbox watchIt\" aria-label=\"Watch It\">\n<p>This video will help you understand confounding factors.<\/p>\n<p><iframe loading=\"lazy\" id=\"oembed-2\" title=\"Clearing Up Confounding\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/fjdb4ID7HVg?feature=oembed&#38;rel=0\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<\/section>\n","protected":false},"author":13,"menu_order":13,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":743,"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\/757"}],"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":7,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/757\/revisions"}],"predecessor-version":[{"id":6578,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/757\/revisions\/6578"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/743"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/757\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=757"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=757"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=757"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}