Why are Robust Statistical Methods Important in Epidemiology?
Epidemiological data can be messy and complex. Traditional statistical methods often assume that data are normally distributed, free of outliers, and complete. However, real-world data rarely meet these assumptions. Robust methods are designed to handle such complexities, making them essential tools for epidemiologists. These methods can mitigate the effects of [outliers](), handle [missing data](), and provide more reliable estimates of [effect sizes]() and [associations]().
