Confounders are crucial to identify and control because they can lead to bias in study results. If not accounted for, they may cause researchers to draw incorrect conclusions about the relationship between an exposure and an outcome. For example, not adjusting for age in a study comparing mortality rates between two populations can lead to misleading results if one population is significantly older than the other.
