Correlation is not causation — Data, 14–17 years
A pattern between two measurements does not by itself prove that one causes the other. Data, 14–17 years.
The idea
Correlation describes a pattern in which two variables change together. A positive correlation means larger values of one tend to go with larger values of the other; a negative one means larger values tend to go with smaller values. It describes association, not the reason behind it.
Why we need it
Finding patterns can help us make predictions, but confusing a pattern with a cause can lead to bad decisions. Two variables may be affected by a third factor, one may cause the other, or the pattern may be accidental. Careful data work asks what else could explain the link before recommending an action.
Worked example
Suppose a town records ice-cream sales and swimming-pool visits each month. Both rise from 1,000 and 200 in spring to 3,000 and 600 in summer, then fall. The variables are positively correlated. It would be wrong to conclude that buying ice cream causes swimming; hot weather increases both, acting as a third factor.
The common trap
The tempting mistake is to say, “A increases when B increases, so A causes B.” That feels reasonable because causes often do create patterns. But the data may not show which variable came first, may omit a third variable, or may include a coincidence. A graph can reveal a relationship; it cannot settle causation by itself.
Where it is used
Researchers use correlations to spot possible links between exercise and health, rainfall and crop growth, or sleep and concentration. These links can suggest useful questions and experiments. Medical, social and business decisions need stronger evidence when changing one thing is expected to cause another, such as a controlled trial or a well-designed comparison.
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