The trimmed mean is a statistical measure of central tendency calculated by discarding a specific percentage of the lowest and highest values from a dataset and then computing the arithmetic mean of the remaining values. As an illustration, consider a dataset of ten values. Calculating a 10% trimmed mean involves removing the lowest 10% (one value) and the highest 10% (one value) and then averaging the remaining eight values.
This calculation offers resilience against outliers, extreme values that can disproportionately influence the standard arithmetic mean. By removing these extreme data points, the trimmed mean provides a more robust representation of the typical value within the dataset. The use of this measure is beneficial in scenarios where data might be prone to errors or when a dataset contains genuine extreme values that are not representative of the population being studied. Historically, such measures have gained favor in competitive settings like judging, where subjective scores are often given and the presence of biased judges can introduce outliers.