Resistant estimators are statistical tools designed to provide reliable estimates in the presence of outliers or non-normal data distributions. Unlike traditional estimators, which can be heavily influenced by extreme values, resistant estimators focus on the central tendency of the data, ensuring that the results remain stable and meaningful. Common examples of resistant estimators include the median and trimmed mean. The median is the middle value in a sorted dataset, while the trimmed mean removes a certain percentage of the highest and lowest values before calculating the average. These methods help maintain accuracy in data analysis, especially in real-world scenarios where outliers are common.