Robust estimation is a statistical technique used to obtain reliable estimates of parameters, even when the data contains outliers or is not normally distributed. Unlike traditional methods, which can be heavily influenced by extreme values, robust estimation focuses on providing results that are more stable and accurate under various conditions. This approach often employs methods such as M-estimators or trimmed means to minimize the impact of anomalies in the data. By doing so, robust estimation helps researchers and analysts make better-informed decisions based on their data, ensuring that conclusions drawn are valid and trustworthy.