The primary issue with overmatching is that it can reduce statistical power and make it difficult to identify true associations. By matching on variables that are associated with the exposure but not with the disease, researchers can inadvertently eliminate the variability needed to detect a real difference. This can lead to Type II errors, where true associations are missed.
