In epidemiology, failure to adjust for confounders can lead to biased results. Confounders are variables that are related to both the exposure and the outcome, and if not properly adjusted, they can distort the true association. For example, in a study examining the link between smoking and lung cancer, age could be a confounder since it is associated with both smoking habits and cancer risk. Multivariable adjustment helps to control for these confounding variables, providing a clearer picture of the causative relationship.
