Confounding is distortion of an exposure-outcome association by common causes or associated background variables.
A confounder predicts both treatment assignment and outcome and can create a noncausal association.

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Confounding occurs in statistical analysis when the effect of one variable is mixed up with the effect of another variable. This can lead to misleading conclusions about the relationship between the variables being studied. In other words, a confounder is an external factor that is associated with both the independent variable (the one being manipulated or the presumed cause) and the dependent variable (the one being measured or the presumed effect).
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