Correlation<\/strong> means there is a statistical association between variables. Causation<\/strong> means that a change in one variable causes a change in another variable.<\/p>\n
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 and interpret scientific research.<\/p>\n
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Correlation<\/strong> describes an association between variables<\/a>: when one variable changes, so does the other. A correlation is a 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
A correlation doesn\u2019t imply causation, but causation always implies correlation.<\/p>\n
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
The third variable problem<\/strong> means that a 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.<\/p>\n
You\u2019ll need to use an appropriate research design<\/a> to distinguish between correlational and causal relationships.<\/p>\n
Correlational research designs<\/a> can only demonstrate correlational links between variables, while experimental designs<\/a> can test causation.<\/p>\n
In a correlational research design, you collect data on your variables without manipulating them.<\/p>\n