What is the difference between Dispersion and Skewness?

Briefly, the dispersion can be defined as a measure of distribution range around a central location. On the other hand, Skewness can be defined as a measure of asymmetry occurring in a statistical distribution. Let’s discuss further dispersion; it refers to the measure of the distribution of data, that is, how close or far a set of data is from themselves within a range. It determines the variability majorly within the items of a set of data around its central point. The standard measure of dispersion is variance, and other measures of dispersion include Average Deviation and Range. On the other hand, skewness can be described as an asymmetry measure of distribution around a particular point. A distribution can be strongly asymmetry, mildly asymmetry, or symmetric. So, you can use skewness to compute a distribution’s measure of asymmetry. The value of a skewness may be undefined, negative, or positive, depending on where the data point is skewed, maybe to the left or right.

Dispersion and skewness are terms that are used when you want to describe the relationship of different sets of data with one another. When you say dispersion, you mean to say that you would like to know the degree of variation of the different values that are available.

Usually, this would be compared with the average value that has been received because of the data. When you say skewness, you are referring to how the data will be distributed normally in a data set. Remember that you will also use skewness when you want to know the overall symmetry of the data when used in a statistical distribution.