Which measurement is most affected by extreme values in a dataset?

The mean is defined as the average of all values in a dataset, calculated by summing all the values and dividing by the number of values. This calculation means that every individual data point contributes to the mean, which makes it sensitive to extreme values, often referred to as outliers. When extreme values are present in the dataset, they can pull the mean in their direction, thereby skewing the depiction of the dataset's central tendency.

For instance, if most values in a dataset are relatively small but one value is extremely large, the mean will be significantly higher than what might be a more representative average of the majority of the data points. This sensitivity to outliers is why the mean is less reliable in describing the general trend of data when extreme values are present.

In contrast, the median, which represents the middle value when all data points are arranged in order, is less affected by extreme values. The mode, indicating the most frequently occurring value, and the range, which measures the difference between the maximum and minimum values, are also influenced by extreme data but not to the same extent as the mean. Therefore, the mean is the measurement that is most notably affected by extreme values in a dataset.