Skewness

Skewness is a measure of how much the data concentration is away from the center of the spread.

There are different ways to measure skewness, and we are not going into its details. Rather we will explore the meaning of it.

Right Skewness

Suppose the concentration is to the left of the center, we call it a negative skew. In this case the Mean is bigger than the Median. 

See the chart below for an example of this.

The reason we call the above right skewed, is because the outliers are to the right. It may sound confusing, but see that the skew is actually felt in the tail than in the rest of the chart.

Left Skewness

What happens when the concentration of data is to the right side? It is called positive (or left) skew. In this case the Mean is less than the Median.

See an example chart below.

We discussed skewness in a theoretical way here, and haven't really calculated it. It's because the calculation is a bit complex, and we don't want to get down to the heavier math in this course.

Also you have many calculators to help you with this. ncalculators is one such calculator that allows you to enter your data set and get the skewness.

Let's conclude our discussion on skewness and move on to the next measure of asymmetry.