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Confounding variables are ‘hidden’ factors that can affect the results of a study, making it hard to tell what’s really causing an outcome. They can create a false link between two things by influencing both of them.
Example: If a study finds that people who drink coffee have a higher risk of heart disease, smoking could be a confounding variable if coffee drinkers are also more likely to smoke. In this case, it might be smoking—not coffee—that's increasing the risk.
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