Trimmed mean, also known as winsorized mean or truncated mean, is a statistical measure used to calculate the average or central tendency of a data set by removing a certain percentage of extreme values or outliers from both ends of the distribution, ensuring a more robust and stable estimate of the data's central value. Trimmed mean involves trimming or discarding a specified percentage of data points, typically 5% to 25%, from the upper and lower tails of the data set before calculating the mean, reducing the influence of outliers or extreme values on the calculated average. Trimmed mean is less sensitive to extreme observations compared to other measures of central tendency, such as the arithmetic mean or median, making it suitable for skewed or asymmetric data distributions where outliers may distort the results or affect the interpretation of the data. Trimmed mean is commonly used in economics, finance, research, and data analysis to mitigate the impact of outliers, improve data quality, and enhance the reliability and accuracy of statistical estimates, particularly in situations where extreme values may arise due to measurement errors, sampling bias, or random fluctuations. Trimmed mean calculations involve sorting the data, removing the specified percentage of observations from both ends, and calculating the mean of the remaining data points, providing a more robust and representative measure of central tendency that reflects the underlying distribution of the data and minimizes the influence of outliers on statistical analysis and decision-making.