Overview
Skewness is one of the most important topics in mathematical statistics. The word skewness means lack of symmetry.
It is an important part of statistics. we use this theory in data sciences, data analytics, big data, e-commerce, business fields, etc.
Nowadays we use data in different business models to achieve the target, and the concept of skewness can give sort of ideas how the data is spread and how someone can use it to draw meaningful decisions.
Definition
Skewness is the measurement of the symmetry of the given distribution.
In a distribution, if the tail on one side of the mode(top point of a distribution) is fatter than other then we can say that the distribution is skewed.
When there is no skewness the curve is known symmetric curve, and it looks like-
No skewness
And when the distribution looks like the following figure-
Positively skewed
Then it is positively skewed.
And for negative skewed data, the distribution will look like-
Negatively skewed
Measurement of skewness
To measure the skewness we use Karl Pearson’s coefficient of skewness-
Note- The value of Karl Pearson’s coefficient of skewness lies between -1 to +1.
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