Correlation vs. Causation | Difference, Designs & Examples
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 “correlation doesn’t imply causation.” Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate sources and interpret scientific research.
What’s the difference?
Correlation describes an association between types of variables: when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. These variables change together: they covary. But this covariation isn’t necessarily due to a direct or indirect causal link.
Causation 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.
Why doesn’t correlation mean causation?
There are two main reasons why correlation isn’t causation. These problems are important to identify for drawing sound scientific conclusions from research.
The third variable problem means that a confounding variable 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 research biases to creep into your work.
The directionality problem occurs when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it’s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.
You’ll need to use an appropriate research design to distinguish between correlational and causal relationships:
- Correlational research designs can only demonstrate correlational links between variables.
- Experimental designs can test causation.
In a correlational research design, you collect data on your variables without manipulating them.
Correlational research is usually high in external validity, so you can generalize your findings to real life settings. But these studies are low in internal validity, which makes it difficult to causally connect changes in one variable to changes in the other.
These research designs are commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren’t expected to be causal.
Third variable problem
Without controlled experiments, it’s hard to say whether it was the variable you’re interested in that caused changes in another variable. Extraneous variables are any third variable or omitted variable other than your variables of interest that could affect your results.
Limited control 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’t.
When two variables are correlated, all you can say is that changes in one variable occur alongside changes in the other.
Regression to the mean
Regression to the mean 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.
A spurious correlation is when two variables appear to be related through hidden third variables or simply by coincidence.
When you analyze correlations in a large dataset with many variables, the chances of finding at least one statistically significant result are high. In this case, you’re more likely to make a type I error. This means erroneously concluding there is a true correlation between variables in the population based on skewed sample data.
To demonstrate causation, you need to show a directional relationship with no alternative explanations. This relationship can be unidirectional, with one variable impacting the other, or bidirectional, where both variables impact each other.
A correlational design won’t be able to distinguish between any of these possibilities, but an experimental design can test each possible direction, one at a time.
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.
Experiments are high in internal validity, so cause-and-effect relationships can be demonstrated with reasonable confidence.
You can establish directionality in one direction because you manipulate an independent variable before measuring the change in a dependent variable.
In a controlled experiment, you can also eliminate the influence of third variables by using random assignment and control groups.
Random assignment helps distribute participant characteristics evenly between groups so that they’re similar and comparable. A control group lets you compare the experimental manipulation to a similar treatment or no treatment (or a placebo, to control for the placebo effect).
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Frequently asked questions about correlation and causation
- What is a correlation?
A correlation reflects the strength and/or direction of the association between two or more variables.
- A positive correlation means that both variables change in the same direction.
- A negative correlation means that the variables change in opposite directions.
- A zero correlation means there’s no relationship between the variables.
- What’s the difference between correlation and causation?
Correlation describes an association between variables: when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.
Causation 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’s also a causal link between them.
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—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy.
- Why doesn’t correlation imply causation?
The third variable and directionality problems are two main reasons why correlation isn’t causation.
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.
The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.
- What’s the difference between correlational and experimental research?
- In an experimental design, you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
- In a correlational design, you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.
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