{"id":312221,"date":"2021-07-12T13:05:10","date_gmt":"2021-07-12T11:05:10","guid":{"rendered":"https:\/\/www.scribbr.nl\/?p=312221"},"modified":"2023-06-22T09:58:05","modified_gmt":"2023-06-22T07:58:05","slug":"correlation-vs-causation","status":"publish","type":"post","link":"https:\/\/www.scribbr.com\/methodology\/correlation-vs-causation\/","title":{"rendered":"Correlation vs. Causation | Difference, Designs &#038; Examples"},"content":{"rendered":"<p><strong>Correlation<\/strong> means there is a statistical association between variables. <strong>Causation<\/strong> means that a change in one variable causes a change in another variable.<\/p>\n<p>In research, you might have come across the phrase \u201ccorrelation doesn\u2019t imply causation.\u201d Correlation and causation are two related ideas, but understanding their differences will help you critically <a href=https://www.scribbr.com/"https:////www.scribbr.com//working-with-sources//evaluating-sources///">evaluate sources<\/a> and interpret scientific research.<\/p>\n<p><!--more--><\/p>\n<h2>What\u2019s the difference?<\/h2>\n<p><strong>Correlation<\/strong> describes an association between <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//types-of-variables///">types of variables<\/a>: when one variable changes, so does the other. A correlation is a <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//parameter-vs-statistic///">statistical indicator<\/a> of the relationship between variables. These variables change together: they covary. But this covariation isn\u2019t necessarily due to a direct or indirect causal link.<\/p>\n<p><strong>Causation<\/strong> means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The two variables are correlated with each other and there is also a causal link between them.<\/p>\n<figure class=\"kb-textbox border-left green\">A correlation doesn\u2019t imply causation, but causation always implies correlation.<\/figure>\n<h2 id=\"why\">Why doesn\u2019t correlation mean causation?<\/h2>\n<p>There are two main reasons why correlation isn\u2019t causation. These problems are important to identify for drawing sound scientific conclusions from research.<\/p>\n<p>The<strong> third variable problem<\/strong> means that a <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//confounding-variables///">confounding variable<\/a> affects both variables to make them seem causally related when they are not. For example, ice cream sales and violent crime rates are closely correlated, but they are not causally linked with each other. Instead, hot temperatures, a third variable, affects both variables separately. Failing to account for third variables can lead <a href=https://www.scribbr.com/"https:////www.scribbr.com//faq-category//research-bias///">research biases<\/a> to creep into your work.<\/p>\n<p>The <strong>directionality problem<\/strong> occurs when two variables correlate and might actually have a causal relationship, but it\u2019s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it\u2019s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.<\/p>\n<p>You\u2019ll need to use an appropriate<a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//research-design///"> research design<\/a> to distinguish between correlational and causal relationships:<\/p>\n<ul>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//correlational-research///">Correlational research designs<\/a> can only demonstrate correlational links between variables.<\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//experimental-design///">Experimental designs<\/a> can test causation.<\/li>\n<\/ul>\n<h2 id=\"correlational-research\">Correlational research<\/h2>\n<p>In a correlational research design, you collect data on your variables without manipulating them.<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Example: Correlational research<\/figcaption>You collect <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//survey-research///">survey data<\/a> to investigate whether there is a relationship between physical activity levels and self esteem. You ask participants about their current levels of exercise and measure their self-esteem using an inventory.<\/p>\n<p>You find that physical activity level is positively correlated with self esteem: lower levels of physical activity are associated with lower self esteem, while higher levels of physical activity are associated with higher self esteem.<\/figure>\n<p>Correlational research is usually high in <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//external-validity///">external validity<\/a>, so you can <a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//generalizability///">generalize<\/a> your findings to real life settings. But these studies are low in <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//internal-validity///">internal validity<\/a>, which makes it difficult to causally connect changes in one variable to changes in the other.<\/p>\n<p>These research designs are commonly used when it\u2019s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren\u2019t expected to be causal.<\/p>\n<figure class=\"kb-textbox border-left blue\"><figcaption>Example: Correlational research<\/figcaption>To study whether consuming violent media is related to aggression, you <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//data-collection///">collect data<\/a> on children\u2019s video game use and <a href=https://www.scribbr.com/"https:////www.scribbr.com//commonly-confused-words//there-their-theyre///">their<\/a> behavioral tendencies. You ask parents to report the number of weekly hours their child spent playing violent video games, and you survey parents and teachers on the children&#8217;s behaviors.<\/p>\n<p>You find a positive correlation between the variables: children who spend more time playing violent video games have higher rates of aggressive behavior.<\/figure>\n<h2 id=\"third-variable\">Third variable problem<\/h2>\n<p>Without controlled experiments, it&#8217;s hard to say whether it was the variable you\u2019re interested in that caused changes in another variable. <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//extraneous-variables///">Extraneous variables<\/a> are any <strong>third variable<\/strong> or <a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//omitted-variable-bias///">omitted variable<\/a> other than your variables of interest that could affect your results.<\/p>\n<p>Limited <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//control-variable///">control<\/a> in correlational research means that extraneous or confounding variables serve as alternative explanations for the results. Confounding variables can make it seem as though a correlational relationship is causal when it isn\u2019t.<\/p>\n<figure class=\"kb-textbox border-left blue\"><figcaption>Example: Extraneous and confounding variables<\/figcaption>In your study on violent video games and aggression, parental attention is a confounding variable that could influence how much children use violent video games and their behavioral tendencies. Low quality parental attention can increase both violent video game use and aggressive behaviors in children.<\/p>\n<p>But it\u2019s not something you control for, so you can only draw a conclusion of correlation between your main variables.<\/figure>\n<p>When two variables are correlated, all you can say is that changes in one variable occur alongside changes in the other.<\/p>\n<h2 id=\"regression-to-the-mean\">Regression to the mean<\/h2>\n<p><strong><a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//regression-to-the-mean///">Regression to the mean<\/a><\/strong> is observed when variables that are extremely higher or extremely lower than average on the first measurement move closer to the average on the second measurement. Particularly in research that intentionally focuses on the most extreme cases or events, RTM should always be considered as a possible cause of an observed change.<\/p>\n<figure class=\"kb-textbox border-left blue\"><figcaption>Example: Regression to the mean<\/figcaption>Regression to the mean can explain the so-called \u201c<em>Sports Illustrated<\/em> jinx.\u201d This urban legend claims that athletes or teams that appear on the cover of the sports magazine will perform poorly in their next game.<\/p>\n<p>Players or teams featured on the cover of <em>SI<\/em> have earned their place by performing exceptionally well. But athletic success is a mix of skill and luck, and even the best players don\u2019t always win.<\/p>\n<p>Chances are that good luck will not continue indefinitely, and neither can exceptional success.<\/p>\n<p>In other words, due to RTM, a great performance is more likely to be followed by a mediocre one than another great one, giving the impression that appearing on the cover brings bad luck.<\/figure>\n<h2 id=\"spurious-correlation\">Spurious correlations<\/h2>\n<p>A <strong>spurious correlation<\/strong> is when two variables appear to be related through hidden third variables or simply by coincidence.<\/p>\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Spurious correlation<\/figcaption>In Germany and Denmark, statistical evidence shows a clear positive correlation between the population of storks and the birth rate spanning decades. As the stork population fluctuates, so does the number of newborns. How do you account for this pattern?<\/p>\n<p>The <a href=https://www.scribbr.com/"https:////web.stanford.edu//class//hrp259//2007//regression//storke.pdf/">Theory of the Stork<\/a> draws a simple causal link between the variables to argue that storks physically deliver babies. This satirical study shows why you can\u2019t conclude causation from correlational research alone.<\/p>\n<p>In reality, the correlation may be explained by third variables (such as weather patterns, environmental developments, etc.) that caused an increase in both the stork and human populations, or the link may be purely coincidental.<\/figure>\n<p>When you analyze correlations in a large dataset with many variables, the chances of finding at least one <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//statistical-significance///">statistically significant<\/a> result are high. In this case, you\u2019re more likely to make a <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//type-i-and-type-ii-errors///">type I error<\/a>. This means erroneously concluding there is a true correlation between variables in the <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//population-vs-sample///">population<\/a> based on <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//skewness///">skewed<\/a> sample data.<\/p>\n<h2>Directionality problem<\/h2>\n<p>To demonstrate causation, you need to show a <strong>directional relationship<\/strong> with no alternative explanations. This relationship can be unidirectional, with one variable impacting the other, or bidirectional, where both variables impact each other.<\/p>\n<p>A correlational design won\u2019t be able to distinguish between any of these possibilities, but an experimental design can test each possible direction, one at a time.<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Example: Directionality problem<\/figcaption>The variables of physical activity and self esteem can be causally related in three ways:<\/p>\n<ul>\n<li aria-level=\"1\">Physical activity may affect self esteem<\/li>\n<li aria-level=\"1\">Self esteem may affect physical activity<\/li>\n<li aria-level=\"1\">Physical activity and self esteem may both <a href=https://www.scribbr.com/"https:////www.scribbr.com//commonly-confused-words//affect-vs-effect///">affect<\/a> each other<\/li>\n<\/ul>\n<\/figure>\n<p>In correlational research, the directionality of a relationship is unclear because there is limited researcher control. You might risk concluding reverse causality, the wrong direction of the relationship.<\/p>\n<h2 id=\"causal-research\">Causal research<\/h2>\n<p>Causal links between variables can only be truly demonstrated with <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//controlled-experiment///">controlled experiments<\/a>. Experiments test formal predictions, called <a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//hypothesis-testing///">hypotheses<\/a>, to establish causality in one direction at a time.<\/p>\n<p>Experiments are high in<a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//internal-validity///"> internal validity<\/a>, so cause-and-effect relationships can be demonstrated with reasonable confidence.<\/p>\n<p>You can establish directionality in one direction because you manipulate an <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//independent-and-dependent-variables///">independent variable<\/a> before measuring the change in a dependent variable.<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Example: Testing directionality in an experimental design<\/figcaption>You believe that physical activity level affects self esteem, so you test this hypothesis in an experiment. You apply a physical activity intervention and measure changes in self esteem. To establish directionality, your physical activity intervention has to come before any observed change in self esteem.<\/p>\n<p>To test whether this relationship is bidirectional, you\u2019ll need to design a new experiment assessing whether self esteem can impact physical activity level.<\/figure>\n<p>In a controlled experiment, you can also eliminate the influence of third variables by using random assignment and control groups.<\/p>\n<p><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//random-assignment///">Random assignment<\/a> helps distribute participant characteristics evenly between groups so that they\u2019re similar and comparable. A <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//control-group///">control group<\/a> lets you compare the experimental manipulation to a similar treatment or no treatment (or a placebo, to control for the <a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//placebo-effect///">placebo effect<\/a>).<\/p>\n<figure class=\"kb-textbox border-left green\"><figcaption>Example: Controlling third variables in an experimental design<\/figcaption>You randomly place each participant into a control group or an experimental group. Random assignment removes the effects of third variable participant characteristics such as age or mental health status that might influence your results.<\/p>\n<p>The control group receives an unrelated, comparable intervention, while the experimental group receives the physical activity intervention. By keeping all variables constant between groups, except for your independent variable treatment, any differences between groups can be attributed to your intervention.<\/figure>\n<h2 id=\"other\">Other interesting articles<\/h2>\n<p data-pm-slice=\"1 1 []\">If you want to know more about <a href=https://www.scribbr.com/"https:////www.scribbr.com//category//statistics///" class=\"ProsemirrorEditor-link\">statistics<\/a>, <a href=https://www.scribbr.com/"https:////www.scribbr.com//category//methodology///" class=\"ProsemirrorEditor-link\">methodology<\/a>, or <a href=https://www.scribbr.com/"https:////www.scribbr.com//faq-category//research-bias///" class=\"ProsemirrorEditor-link\">research bias<\/a>, make sure to check out some of our other articles with explanations and examples.<\/p>\n<div class=\"container\">\n<div class=\"row\">\n<div class=\"col-md\" style=\"background-color: #e8f2fc; padding: 1.27rem 1.56rem; line-height: 1.8; border-radius: .5rem; margin-right: 0.5rem; margin-top: 0.5rem;\">\n<p><strong><em class=\"fa fa-bar-chart\" aria-hidden=\"true\"><\/em>Statistics<\/strong><\/p>\n<ul style=\"padding: 5px;\">\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//chi-square-test-of-independence///">Chi square test of independence<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//statistical-power///">Statistical power<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//descriptive-statistics///">Descriptive statistics<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//degrees-of-freedom///">Degrees of freedom<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//pearson-correlation-coefficient///">Pearson correlation<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//null-and-alternative-hypotheses///">Null hypothesis<\/a><\/li>\n<\/ul>\n<\/div>\n<div class=\"col-md\" style=\"background-color: #e8f2fc; padding: 1.27rem 1.56rem; line-height: 1.8; border-radius: .5rem; margin-right: 0.5rem; margin-top: 0.5rem;\">\n<p><strong><em class=\"fa fa-list-ul\" aria-hidden=\"true\"><\/em> Methodology<\/strong><\/p>\n<ul style=\"padding: 5px;\">\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//double-blind-study///">Double-blind study<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//case-control-study///">Case-control study<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//research-ethics///">Research ethics<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//data-collection///">Data collection<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//statistics//hypothesis-testing///">Hypothesis testing<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//structured-interview///">Structured interviews<\/a><\/li>\n<\/ul>\n<\/div>\n<div class=\"col-md\" style=\"background-color: #e8f2fc; padding: 1.27rem 1.56rem; line-height: 1.8; border-radius: .5rem; margin-right: 0.5rem; margin-top: 0.5rem;\">\n<p><strong><em class=\"fa fa-globe\" aria-hidden=\"true\"><\/em> Research bias<\/strong><\/p>\n<ul style=\"padding: 5px;\">\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//hawthorne-effect///">Hawthorne effect<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//unconscious-bias///">Unconscious bias<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//recall-bias///">Recall bias<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//halo-effect///">Halo effect<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//self-serving-bias///">Self-serving bias<\/a><\/li>\n<li><a href=https://www.scribbr.com/"https:////www.scribbr.com//research-bias//information-bias///">Information bias<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n    <h2>Frequently asked questions about correlation and causation<\/h2>\n<dl class=\"faq-list faq-list--arrows w-100 my-5\" itemscope itemtype=\"https:\/\/schema.org\/FAQPage\">\n            <div itemscope itemprop=\"mainEntity\" itemtype=\"https:\/\/schema.org\/Question\">\n            <dt id=\"what-is-a-correlation\" class=\"faq-list__title\">\n                <a href=https://www.scribbr.com/"https:////www.scribbr.com//frequently-asked-questions//what-is-a-correlation///" class=\"faq-list__link qa-faq-anchor\">\n                    <span itemprop=\"name\">\n                        What is a correlation?\n                    <\/span>\n                <\/a>\n            <\/dt>\n            <dd id=\"what-is-a-correlation-answer\" class=\"faq-list__answer qa-faq-answer kb-article-styles\" itemscope itemprop=\"acceptedAnswer\" itemtype=\"https:\/\/schema.org\/Answer\">\n                <div itemprop=\"text\">\n                                        <p>A <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//correlation-vs-causation///">correlation<\/a> reflects the strength and\/or direction of the association between two or more variables.<\/p>\n<ul>\n<li>A <strong>positive correlation<\/strong> means that both variables change in the same direction.<\/li>\n<li>A<strong> negative correlation<\/strong> means that the variables change in opposite directions.<\/li>\n<li>A <strong>zero correlation<\/strong> means there\u2019s no relationship between the variables.<\/li>\n<\/ul>\n\n                <\/div>\n            <\/dd>\n        <\/div>\n            <div itemscope itemprop=\"mainEntity\" itemtype=\"https:\/\/schema.org\/Question\">\n            <dt id=\"difference-between-correlation-and-causation\" class=\"faq-list__title\">\n                <a href=https://www.scribbr.com/"https:////www.scribbr.com//frequently-asked-questions//difference-between-correlation-and-causation///" class=\"faq-list__link qa-faq-anchor\">\n                    <span itemprop=\"name\">\n                        What\u2019s the difference between correlation and causation?\n                    <\/span>\n                <\/a>\n            <\/dt>\n            <dd id=\"difference-between-correlation-and-causation-answer\" class=\"faq-list__answer qa-faq-answer kb-article-styles\" itemscope itemprop=\"acceptedAnswer\" itemtype=\"https:\/\/schema.org\/Answer\">\n                <div itemprop=\"text\">\n                                        <p><b>Correlation<\/b><span style=\"font-weight: 400;\"> describes an association between <\/span><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//types-of-variables///"><span style=\"font-weight: 400;\">variables<\/span><\/a><span style=\"font-weight: 400;\">: when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.<\/span><\/p>\n<p><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//correlation-vs-causation///"><b>Causation<\/b><\/a><span style=\"font-weight: 400;\"> means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there\u2019s also a causal link between them.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B\u2014but A doesn\u2019t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to <\/span><a href=https://www.scribbr.com/"https:////www.scribbr.com//fallacies//false-cause-fallacy///"><b>false cause fallacy<\/b><\/a>.<\/p>\n\n                <\/div>\n            <\/dd>\n        <\/div>\n            <div itemscope itemprop=\"mainEntity\" itemtype=\"https:\/\/schema.org\/Question\">\n            <dt id=\"why-doesnt-correlation-imply-causation\" class=\"faq-list__title\">\n                <a href=https://www.scribbr.com/"https:////www.scribbr.com//frequently-asked-questions//why-doesnt-correlation-imply-causation///" class=\"faq-list__link qa-faq-anchor\">\n                    <span itemprop=\"name\">\n                        Why doesn&#8217;t correlation imply causation?\n                    <\/span>\n                <\/a>\n            <\/dt>\n            <dd id=\"why-doesnt-correlation-imply-causation-answer\" class=\"faq-list__answer qa-faq-answer kb-article-styles\" itemscope itemprop=\"acceptedAnswer\" itemtype=\"https:\/\/schema.org\/Answer\">\n                <div itemprop=\"text\">\n                                        <p>The third variable and directionality problems are two main reasons why <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//correlation-vs-causation///">correlation isn\u2019t causation<\/a>.<\/p>\n<p>The<strong> third variable<\/strong> problem means that a <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//confounding-variables///">confounding variable<\/a> affects both variables to make them seem causally related when they are not.<\/p>\n<p>The <strong>directionality problem<\/strong> is when two variables correlate and might actually have a causal relationship, but it\u2019s impossible to conclude which variable causes changes in the other.<\/p>\n\n                <\/div>\n            <\/dd>\n        <\/div>\n            <div itemscope itemprop=\"mainEntity\" itemtype=\"https:\/\/schema.org\/Question\">\n            <dt id=\"correlational-vs-experimental-research\" class=\"faq-list__title\">\n                <a href=https://www.scribbr.com/"https:////www.scribbr.com//frequently-asked-questions//correlational-vs-experimental-research///" class=\"faq-list__link qa-faq-anchor\">\n                    <span itemprop=\"name\">\n                        What\u2019s the difference between correlational and experimental research?\n                    <\/span>\n                <\/a>\n            <\/dt>\n            <dd id=\"correlational-vs-experimental-research-answer\" class=\"faq-list__answer qa-faq-answer kb-article-styles\" itemscope itemprop=\"acceptedAnswer\" itemtype=\"https:\/\/schema.org\/Answer\">\n                <div itemprop=\"text\">\n                                        <p><a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//controlled-experiment///">Controlled experiments<\/a> establish causality, whereas <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//correlational-research///">correlational studies<\/a> only show associations between variables.<\/p>\n<ul>\n<li>In an <strong>experimental design<\/strong>, you manipulate an <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//independent-and-dependent-variables///">independent variable<\/a> and measure its effect on a dependent variable. Other variables are <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//control-variable///">controlled<\/a> so they can&#8217;t impact the results.<\/li>\n<li>In a <strong>correlational design<\/strong>, you measure variables without manipulating any of them. You can test whether your variables change together, but you can&#8217;t be sure that one variable caused a change in another.<\/li>\n<\/ul>\n<p>In general, correlational research is high in <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//external-validity///">external validity<\/a> while experimental research is high in <a href=https://www.scribbr.com/"https:////www.scribbr.com//methodology//internal-validity///">internal validity<\/a>.<\/p>\n\n                <\/div>\n            <\/dd>\n        <\/div>\n    <\/dl>\n","protected":false},"excerpt":{"rendered":"<p>Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable. In research, you might have come across the phrase \u201ccorrelation doesn\u2019t imply causation.\u201d Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate sources and interpret [&hellip;]<\/p>\n","protected":false},"author":115,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":""},"categories":[23650],"tags":[],"acf":[],"yoast_head":"<title>Correlation vs. Causation | Difference, Designs &amp; Examples<\/title>\n<!-- Added by HTTrack --><meta http-equiv="content-type" content="text/html;charset=UTF-8" /><!-- /Added by HTTrack -->
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