What characterizes a skewed distribution?

A skewed distribution is characterized by a spread where data clusters to one end. In this type of distribution, the data points are not evenly distributed around the mean; instead, there is a tail on one side. This indicates that most of the values are concentrated on one side of the distribution, leading to either a left-side (negative) skew or a right-side (positive) skew.

For example, in a right-skewed distribution, the tail extends to the right, meaning that a significant number of lower values are present while fewer higher values stretch out the tail. Conversely, in a left-skewed distribution, the opposite occurs, with the tail extending to the left, indicating that there are more higher values and fewer lower values. This clustering of data points influences the measures of central tendency, like the mean, median, and mode, making them all skewed in different ways.

In contrast, symmetrical distributions would show a balanced spread of data around the mean, while equal distribution would imply that data points are uniformly spread across a range, neither clustering nor creating a tail. A distribution with a constant mean does not characterize skewness, as it suggests a stable central tendency regardless of data spread. Thus, distinguishing features of skewness include the