Skewness

 

What is Skewness?


Skewness is asymmetry (lack of symmetry) in a statistical distribution, in which the curve appears distorted or skewed either to the left or to the right. Skewness can be quantified to define the extent to which a distribution differs from a normal distribution.

In a normal distribution, the graph appears as a classical, symmetrical "bell-shaped curve." The mean, or average, and the mode, or maximum point on the curve, are equal.In a perfect normal distribution, the tails on either side of the curve are exact mirror images of each other.

When a distribution is skewed to the left, the tail on the curve's left-hand side is longer than the tail on the right-hand side, and the mean is less than the mode. This situation is also called negative skewness.

When a distribution is skewed to the right, the tail on the curve's right-hand side is longer than the tail on the left-hand side, and the mean is greater than the mode. This situation is also called positive skewness.

Where we use skewness?

Skewness is a descriptive statistic that can be used as indicator of the direction and relative magnitude of distribution deviation from normal distribution.In some statistical studied we have to normalize  data and in that cases this skewness help us a lot.We can detect position of outliers whether they are on left side or right side.

The best example,India has more than 50% of its population below the age of 25 and more than 65% below the age of 35. If you’ll plot the distribution of the age of the population of India, you will find that there is a hump on the left side of distribution and the right side is comparatively planar.

In next post we will see some formulae and examples to find skewness...


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