Which measure is typically considered a better estimator of spread when outliers are present?

The median is recognized as a more robust measure of spread, particularly in the presence of outliers. When data contains extreme values, the mean can be significantly influenced, leading to a distorted representation of the central tendency. This distortion affects measures of spread that rely on the mean, such as variance.

In contrast, the median represents the middle point of a data set when organized in order and is not affected by extreme values at either end. This property allows the median to provide a more reliable indicator of the center of the data, which is also applicable when calculating measures of spread like interquartile range or absolute deviation from the median.

Range and variance are also sensitive to outliers. The range, which is the difference between the maximum and minimum values, will be heavily skewed by any outliers present, while variance, as mentioned earlier, takes the average of the squared deviations from the mean, further amplifying the influence of outliers. Thus, when assessing spread in the presence of outliers, the median serves as a more accurate and stable measure.