A tool used for statistical analysis, this device calculates the average of a dataset after removing a specified percentage of the highest and lowest values. For example, a 10% trimmed mean of the dataset [1, 5, 7, 9, 11, 12, 18, 20] involves discarding the bottom 10% (1) and the top 10% (20) before calculating the average of the remaining numbers. This process mitigates the impact of outliers on the central tendency measure.
Reducing the influence of extreme values creates a more robust measure of central tendency, particularly useful in datasets prone to errors or extreme fluctuations. This method offers a balance between the mean, which can be heavily influenced by outliers, and the median, which completely disregards the magnitude of many data points. The historical context of this statistical approach dates back to robust statistics development aimed at providing stable estimations in the presence of noisy data.