A trimmed mean is a statistical measure that calculates the average of a dataset after removing a certain percentage of the highest and lowest values. This process helps reduce the influence of outliers, which are extreme values that can skew the average. By focusing on the central portion of the data, the trimmed mean provides a more accurate representation of the dataset's typical value. For example, if you have a set of test scores and want to find the trimmed mean, you might remove the lowest 10% and the highest 10% of scores before calculating the average. This method is particularly useful in fields like economics and psychology, where outliers can significantly affect results.