Correlation is not causation — Data, 11–13 years
When two measurements change together, they are correlated. That pattern can be useful, but it does not by itself prove that one measurement causes the other.
Two things moving together
Correlation means that two quantities show a pattern together: when one rises, the other often rises or falls. Causation is stronger: it means changing one helps produce a change in the other. A correlation can exist without that cause.
Why the distinction matters
A pattern can tempt us to tell a simple cause-and-effect story. But a third factor may affect both, or the direction may be reversed. Separating correlation from causation stops us turning an interesting clue into an unsupported claim.
Ice cream and sunburn
A town records more ice-cream sales and more sunburn cases in July than in January. The two numbers rise together, so they are correlated. Ice cream does not cause sunburn: hot, sunny weather encourages both ice-cream eating and time outdoors.
Together does not mean because
The tempting mistake is to say, “A happened when B increased, so A caused B.” It feels convincing because we use cause-and-effect reasoning every day. Data can show a relationship, but proving a cause usually needs careful comparisons or a controlled experiment.
Reading claims carefully
You may see adverts or news stories claiming that a product causes better sleep, higher marks or improved health because users show both. Ask what else could explain the pattern and whether there was a fair comparison. That question protects you from overconfident conclusions.
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