Correlation vs. causation
01 // The difference
Correlation means two variables change together in a pattern — as one goes up, the other tends to go up (or down) too. Causation means one variable's change directly produces the other's change. Every causal relationship shows up as a correlation, but plenty of correlations exist with no causal link at all — the phrase 'correlation does not imply causation' exists exactly because this mistake is so easy to make.
02 // A classic example
Correlation (observed)
Ice cream sales and drowning deaths- Both rise in the summer months
- Both fall in the winter months
- They move together — a real correlation
Actual cause
Hot weather (a confounding variable)- Heat makes people buy more ice cream
- Heat also drives more people to swim
- Ice cream doesn't cause drowning at all
03 // How experiments get closer to causation
- Simply observing two things happening together (an observational study) can only ever establish correlation, not causation.
- A controlled experiment — where one variable is deliberately changed while everything else is held constant — is what lets scientists claim causation, because it rules out other explanations.
- Even strong, repeated correlations are treated cautiously in science until a plausible causal mechanism is identified and tested directly.
04 // Questions people ask
What does 'correlation does not imply causation' mean?
It means that just because two variables change together in a pattern doesn't prove that one is causing the other to change. They might both be driven by a separate, hidden factor, or the relationship could even be coincidental.
How can you tell if a relationship is actually causal?
The strongest evidence comes from a controlled experiment, where researchers deliberately change one variable while holding everything else constant and observe the effect. If changing the variable reliably produces the same result, that supports causation far more than simply observing two things happening together.
What is a confounding variable's role in a false correlation?
A confounding variable is a hidden third factor that independently affects both of the variables that appear correlated, creating the illusion that one causes the other when really both are just responding to the same underlying cause.