What term is used to describe a distribution’s extreme values or outliers that may skew statistical analysis?

The term that is used to describe a distribution’s extreme values or outliers is skewness. Skewness measures the asymmetry of the distribution of values in a dataset. When outliers are present, they can heavily influence the overall shape of the distribution, leading to a situation where one tail is longer or fatter than the other. This skewness can affect resulting statistics, such as the mean, which may not accurately reflect the typical value of the data set due to the presence of these extreme values.

Dispersion refers to the way values are spread out in a dataset, encompassing measures like range, variance, and standard deviation, but it does not specifically identify the effect of outliers. Variance is a quantitative measure of the degree to which data points differ from the mean, but it also does not directly categorize outliers themselves. The range provides the difference between the maximum and minimum values in a dataset but is limited to only two data points and does not account for multiple extreme values or outliers. Therefore, skewness is the most specific term that accurately captures the impact of extreme values in a distribution.