While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.

Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the problem. The trimmed mean is one such example. After sorting the data, the outliers can be removed before calculating the arithmetic mean, the geometric mean, or the harmonic mean. When trimming is carried out symmetrically on the same percentage of the arranged data from both the upper and lower bounds, the data is said to be trimmed by that percentage.