Which measure of central tendency is mostly affected by outliers?

The mean is the measure of central tendency that is most affected by outliers. This is because the mean is calculated by summing all values in a dataset and then dividing by the number of values. When an outlier—an unusually high or low value—enters the dataset, it can significantly skew the result of the mean. For instance, if a dataset includes the numbers 1, 2, 2, 2, and 100, the mean will be much higher due to the presence of 100, even though the majority of the numbers are much lower.

In contrast, the median, which is the middle value when the data points are arranged in order, remains more stable despite the presence of outliers. The mode, being the most frequently occurring value, is not influenced by other values. Standard deviation measures the spread of the data, but while outliers can affect it, it is not a measure of central tendency itself. Therefore, when considering how outliers can shift the perceived "center" of the data, the mean is the most susceptible to their impact.