Which measure of central tendency minimizes the impact of outliers?

The median is the measure of central tendency that minimizes the impact of outliers because it represents the middle value of a data set when it is ordered from smallest to largest. In essence, the median is not affected by extreme values—whether they are very high or very low—because it solely depends on the position of values in the sorted list, rather than their magnitude.

For example, if you have a data set with values like 1, 2, 3, 4, and 100, calculating the mean would yield a higher average that does not represent the majority of the data well, while the median would remain 3, reflecting a better central position amidst the values. This characteristic makes the median particularly useful when dealing with skewed distributions or when outliers are present, as it provides a more robust measure of central tendency under such circumstances.

The other measures, such as the mean and mode, can be significantly affected by outliers. The mean can shift dramatically with the inclusion of one extreme value, and while the mode indicates the most frequently occurring value, it doesn't take into account the overall distribution or the influence of outliers in the dataset. Variance measures the spread of the data points, and while it doesn't directly represent central