Robust Methods
Robust methods are statistical techniques designed to provide reliable results even when the data does not meet certain assumptions, such as normality or homoscedasticity. These methods are particularly useful in real-world scenarios where data can be messy or contain outliers, ensuring that the conclusions drawn are valid and not overly influenced by extreme values.
Common robust methods include median and trimmed means, which reduce the impact of outliers, and robust regression, which adjusts for violations of standard regression assumptions. By using these techniques, researchers can achieve more accurate and trustworthy insights from their data analysis.