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Correlation vs Causation: What the Difference Really Means
Today I came across a post on LinkedIn by a digital specialist. He confidently claimed that with an A/B test, we can determine not just correlation, but true causality. He used words like “certainty” as if statistics were part of Newtonian physics, clear, absolute, unquestionable. I am surprised by that level of confidence. I do not have it.
What is the difference between correlation and causation? Correlation means two things move together; causation means one of them actually produces the other. The trouble is that our tools mostly reveal the first while tempting us to claim the second. In years of performance marketing I have watched a team celebrate a campaign because revenue rose the week it launched, when a seasonal spike had lifted everything at once. I have seen an A/B test reported as proof of causality on a sample far too small to rule out chance. Telling correlation from causation is not an academic detail. It is the difference between a decision that genuinely works and one that only looked as though it did.
Correlation vs Causation: Why We See Causes Where There Are None
Our brain craves order. When something happens after something else, we instinctively think: “the first thing caused the second.” Got a headache? Must have been the coffee. We are built to look for causes, even when they are not there, a stubborn cognitive bias.
From an evolutionary perspective, this makes perfect sense. If you hear a rustle in the bushes, it is safer to assume there is a tiger and run, even if it is just the wind. Evolution has taught us it is better to be wrong than dead. Maybe that is why we tend to see patterns in randomness, connections in the unconnected.
In the Middle Ages, people believed comets brought disaster. Halley’s Comet appeared in 1066, followed by the Battle of Hastings. Case closed.
For centuries, people also believed that storms, plagues, and crop failures were caused by witchcraft. If lightning struck, a cow died, or a child was born with a deformity, society demanded a culprit. Often it was women, unmarried, childless, or simply too independent. They were accused, tortured, and burned. Over 50,000 people, mostly women, were executed for a cause that never existed.
The philosopher David Hume pointed this out long ago: we never see causality. We only get used to the fact that B follows A. But does A actually cause B? That is just our assumption. And statistics? It shows us that two things may correlate or have some sort of relationship, but not which one causes the other. Even experiments do not bring certainty, only higher probability.
Causality is often just a hypothesis. A model. A tool, not the truth.
Common questions on this article's topic
Why is the confident use of the word certainty problematic?
What is the difference between correlation and causation?
Why does the human brain see causes where there are none?
Can A/B tests prove causation?
What does David Hume say about causality?
Why does this matter for professionals working with data?
What are some examples of correlation vs causation?
If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.
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