Why is the median less affected by extreme values in a dataset?

The median is defined as the middle value in a sorted list of numbers. When calculating the median, the data is arranged in ascending order, and the central value is identified. This characteristic is significant because it means that the median is determined by the position of the values in the dataset rather than their magnitude.

Extreme values, or outliers, can heavily influence measures of central tendency like the mean, which involves adding all values together and then dividing by the number of values. Therefore, if there are very high or very low values in the dataset, they can drastically skew the mean. In contrast, since the median focuses only on the middle value(s), it remains stable and unaffected by those extreme high or low values. This makes the median a more robust measure of central tendency in datasets with outliers.

The other options do not accurately explain why the median is less affected by extreme values and would not provide the same stability in the presence of outliers. For instance, options concerning reliance solely on the highest or lowest values or based on frequency miss the essence of the median’s calculation, which is strictly about the middle point in an ordered dataset.