Data cleaning refers to the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. In epidemiology, data is often collected from various sources such as surveys, health records, and laboratory tests, which can lead to a multitude of errors. Data cleaning ensures that the dataset is reliable, accurate, and ready for analysis.