Robust statistical methods are techniques that provide reliable and consistent results even when the assumptions of traditional statistical methods are violated. These methods are particularly useful in epidemiology, where data often contain outliers, missing values, or non-normal distributions. By using robust methods, epidemiologists can draw more accurate conclusions from their data, leading to better public health decisions.