There are a lot of charts floating around, discussing how x is related to y because they are “highly correlated”.
Our brains tend to make nonsensical connections to try and explain the world to us. We draw two lines and claim a relationship, thinking that we have effectively answered the question in the process.
Correlation is not causation.
Correlation is a relationship, in which A moves with B. There are three types:
- Positive: A and B increase/decrease at the same time
- Negative: increase (decrease) in A leads to a decrease (increase) in B
- None: A and B are unrelated
Causation is a cause-and-effect, in which A causes B.
I’ve made a couple of graphs to illustrate the fine line between correlation and causation — and how correlation can insinuate a relationship that might not actually exist.
Correlation versus Causation
Are falling emissions levels impacting Kim Kardashians popularity?
Are McDonalds restaurants causing inflation?
Did Chads drive the fall in interest rates?
Did Twitter users increase Tesla’s share price?
Is the price of bitcoin a leading indicator for avocado sales?
Did farmers stop farming because of the success of the Marvel Movies?
This is an illustration to show that just because data series move together, doesn’t mean that they cause each other — or that they are “related” at all.
Most of these examples show datasets that have no direct relationship to each other — but they look like they do.
Correlation does not necessarily mean causation.