Robust regression is a statistical technique used to analyze data that may contain outliers or violations of assumptions common in traditional regression methods. Unlike ordinary least squares regression, which can be heavily influenced by extreme values, robust regression provides a more reliable estimate of relationships between variables by minimizing the impact of these outliers. This method employs different algorithms, such as M-estimators or RANSAC, to ensure that the results are not skewed by unusual data points. As a result, robust regression is particularly useful in real-world scenarios where data can be messy and imperfect, leading to more accurate and trustworthy conclusions.