Generally, the mean of a dataset is calculated by dividing the sum of all values by the total number of values. However, the mean is very sensitive to the presence of extreme values. For example, the mean height of students is affected by the outliers—too short or too tall students. 

In such cases, the data are first ordered and then the outliers removed. Now, the mean is calculated for the remaining data values. Such a mean is called the trimmed or truncated mean. 

Notice that, here, from a total of ten data values, one from either side of the dataset is trimmed. That is, 10 percent of the data are removed from both the sides. Then, the mean is calculated for the remaining values and is called 10 percent trimmed mean. 

Trimmed mean helps to avoid meaningless fluctuations in the mean. For example, the inflation rate is calculated using the trimmed mean of commodity prices. It ignores the high volatile price changes and smoothens the data, providing the most accurate inflation rate.