Correlation Is Not Causation
Two things can rise and fall in perfect step without either causing the other. Mistaking that pattern for cause is the commonest error in reading evidence.
When A and B move together, three explanations survive: A causes B, B causes A, or a third thing C causes both. Ice-cream sales and drowning deaths rise together — summer drives both.
The pattern is seductive because our minds are built to see causes. A story arrives instantly and feels like understanding; a shared cause or plain coincidence offers no narrative.
That is why the controlled experiment exists: it turns correlation into a causal claim by moving one thing while holding the rest steady. Without it, a correlation is a lead, not a conclusion.
Interrogate the link
Name all three arrows
For any correlation, write out A→B, B→A, and C→both. Force yourself to state the reverse direction and the hidden third cause before accepting the obvious one.
Hunt the lurking variable
Ask what could drive both at once — season, wealth, age, who chose to take part. The best confounder is often something nobody measured.
- What causes both of these?
- Who selected into each group, and why?
Demand an experiment for a causal claim
If the stakes are causal — will this intervention work? — insist on a study that manipulated the cause. Observation alone cannot carry that weight.
The world is full of things that move together and have nothing to do with each other.
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