Robust Statistical Methods

Robust statistical methods are techniques designed to analyze data accurately even when the data has outliers, errors, or unusual points that might skew traditional analyses. Unlike standard methods, which can be heavily influenced by a few extreme values, robust methods provide more reliable results by minimizing the impact of these anomalies. They help ensure that conclusions remain valid and representative of the overall data pattern, making them valuable in real-world situations where data imperfections are common.