Random under sampling involves reducing the number of instances in the majority class to match the number of instances in the minority class. This is done by randomly selecting and removing instances from the majority class until the dataset is balanced. For example, if an epidemiological dataset has 1000 negative cases and 100 positive cases, random under sampling would involve selecting 100 negative cases at random and discarding the rest.