Skewed Distribution: Examples & Definition

Skewed distributions are asymmetrical and have data that clusters toward one end. In this lesson, learn about positively skewed distributions, negatively skewed distributions, and more.
Skewed Data
Imagine that you were interested in studying the annual income of students one year after they have completed their Masters of Business Administration (MBA). You collect data from 400 graduates and find that their yearly income ranges from $20,000 to $150,000. This table summarizes the data that you have collected.

Income of MBA graduates
Data table
Let’s say that you are also interested in examining the number of applications each graduate completed before they found their current job. Using data collected from the same 400 graduates, you find that the number of applications they completed ranges from 1 to 15. This table summarizes the data that you have collected.

Number of applications submitted before finding work
data table
You probably could not tell by looking at the tables, but the data you collected in both of the studies above is skewed.

What Is a Skewed Distribution?
A distribution is said to be skewed when the data points cluster more toward one side of the scale than the other, creating a curve that is not symmetrical. In other words, the right and the left side of the distribution are shaped differently from each other. There are two types of skewed distributions.

A distribution is positively skewed if the scores fall toward the lower side of the scale and there are very few higher scores. Positively skewed data is also referred to as skewed to the right because that is the direction of the ‘long tail end’ of the chart. Let’s create a chart using the yearly income data that we collected from the MBA graduates.

data chart showing positively skewed distribution
You can see that most of the graduates reported annual income between $31,000 and $70,000. You can see that there are very few graduates that make more than $70,000. The yearly income for MBA graduates is positively skewed, and the ‘long tail end’ of the chart points to the right.

A distribution is negatively skewed if the scores fall toward the higher side of the scale and there are very few low scores. Let’s take a look at the chart of the number of applications each graduate completed before they found their current job.

Data chart showing negatively skewed distribution
We can see that most of the graduates completed between 9 and 13 applications. Only 56 out of the 400 graduates completed less than 9 applications. The number of applications completed for MBA graduates is negatively skewed, and the ‘long tail end’ points to the left. Negatively skewed data is also referred to as ‘skewed to the left’ because that is the direction of the ‘long tail end.’

Characteristics of Skewed Distributions
You are probably somewhat familiar with the mean, median, and mode. The mode is the most frequently occurring score in a distribution. The median is the middle value that separates the top 50% of the distribution from the bottom 50%. The mean is the average found by adding all of the scores together and dividing the sum by the total number of scores.

The mean, median, and mode are measures of central tendency that are used to describe a data set. Here are a few key points to remember: