Bias can distort the true association between exposure and outcome, leading to either an overestimation or underestimation of the effect. For instance, selection bias can result in a sample that does not accurately reflect the population, while information bias can lead to incorrect classification of exposure or outcome status. Confounding can give a false impression of a direct association when none exists.
