Correlation is not causation — you’ve probably heard it a million times. But it can be a bit tricky to truly wrap your head around it. Could a simple image help with this?
Nic Cage has done a lot of things in his life, but clearly, the number of movies he shows in has no link to the number of drownings. It’s a simple case of correlation without any causation — a coincidence of sorts.
But wait, there’s much more. Let’s dive in.
Correlation vs causation
Correlation describes a relationship between two things. These things can be pretty much anything. When two variables are correlated, it means that as one changes, the other tends to change in a specific way. Correlations can be positive (both increase or decrease together) or negative (one increases while the other decreases).
Correlated things aren’t necessarily related to each other. In other words, correlation doesn’t imply causation. But it can be easy to think it does! Just think about it, if every time you wash your car it starts to rain, you may be tempted to think your car washing is actually causing the rain.
This website publishes a lot of apparently correlated things, which obviously have nothing in common but seem correlated. It’s a brilliant example of how correlation is not causation.
Obviously, this is a bit of cherry-picking, showing specific intervals where the data fits, and using specific scales and variables. But this is just for the same of creating examples. Oftentimes, the two variables seem more related, but that still doesn’t necessarily mean that they cause one another.
Simply put, correlation is something you observe: you see element A and element B seem to have a similar evolution. Let’s say, for instance, you see people who eat a specific diet having a higher risk of cancer. That’s a reasonable correlation, but you can’t go on inferring causation from that.
Correlation is not causation
Here’s another interesting example of a correlation: mozzarella cheese consumption is linked to civil engineering doctorates, from 2000 to 2009. Unless you’re trying to argue that mozzarella somehow makes people more inclined to look into advanced engineering, this is obviously another case of correlation and not causation.
Causation takes the relationship between two variables a step further. In a causal relationship, changes in one variable actually cause changes in the other. If Variable A causes Variable B, then if you change Variable A, that will result in predictable changes in Variable B.
Let’s take a simple case. You see that people who eat less tend to be thinner. You infer a correlation. You then recruit (willing) participants to eat less and see whether the causation happens. It probably will, so congrats — you’ve established causation!
But in the real world, it’s not so simple. For the experiment to be robust, you need to ensure that it’s not something else causing the change. Perhaps people in the experiment will be more aware of what they eat and eat healthier, not just less. That’s why, in the real world, it can be so difficult to disentangle different variables and see causation between them.
Why it’s so important to differentiate between the two
Understanding the difference between correlation and causation isn’t just important for scientists — it’s important for understanding the world around us.
Misunderstanding the relationship between two variables can lead to incorrect assumptions about how the world work. For instance, thinking that waking up with a headache (Variable A) means you will have a bad day (Variable B) might just be a correlation. The real cause could be lack of sleep or dehydration, which could also lead to a bad day. Knowing the difference allows us to accurately identify causes and effects.
The distinction between correlation and causation is critical for making informed decisions. If a company wrongly believes that a certain action is causing a particular outcome, they might continue to invest in that action, wasting resources. Or, a government might implement policies based on mistaken beliefs about causality, leading to ineffective or harmful results. In your own personal life, you may make similar misinformed decisions.
Lastly, recognizing the difference can prevent us from falling into logical fallacies or being misled by misinformation. The media, advertisements, and even political debates can often present correlations as if they are causal relationships to persuade or influence. By understanding the difference, we can critically evaluate these claims.
In a world inundated with data and information, the ability to distinguish between correlation and causation equips us to better understand reality, make more effective decisions, and be less susceptible to flawed reasoning or manipulative rhetoric.
So there you have it people — correlation and causation are two different things. Don’t draw any conclusions about causation based on correlation alone!