A robust estimator is a statistical method used to estimate parameters of a population while minimizing the influence of outliers or extreme values. Unlike traditional estimators, which can be heavily affected by unusual data points, robust estimators provide more reliable results in the presence of such anomalies. This makes them particularly useful in real-world data analysis, where data can often be messy or contain errors. Common examples of robust estimators include the median and the trimmed mean. These methods focus on the central tendency of the data without being skewed by outliers. By using robust estimators, researchers and analysts can achieve more accurate and trustworthy conclusions from their data, leading to better decision-making in fields like economics, engineering, and social sciences.