-
Why is it important not to assume causality from a correlation result?
Because two variables moving together does not mean one causes the other. Correlation only ...
-
Why is it important not to assume causality from a correlation result?
Because two variables moving together does not mean one causes the other. Correlation only indicates a statistical association, which can be caused by third-party variables, coincidence, or reverse causality, leading to false conclusions, bad decisions, and poor scientific understanding.
2. Can you give an example of a spurious correlation (where two variables seem related but actually aren’t)?
Ex: ice cream sales and drowning deaths both increase during the summer. They appear related, but ice cream does not cause drowning; both are driven by a third, hidden variable: hotter weather. The correlation is coincidental, not causal.