Skewness

Skewness is a part of probability theory. To put it in simple words, skewness is a measure of the asymmetry of the probability distribution. Skewness is visually represented by the curve leaning towards one of the sides of the normal distribution. 

The skewness value can be positive, zero, negative, or undefined. In the probability distribution, the tail plays an important role. The probability that a random variable deviates by a given amount from its expectation is referred to as a tail probability.

A negative skew commonly indicates that the tail is on the left side of the distribution. On the contrary, a positive skew indicates that the tail is on the right side.

For example, a zero value means that the tails on both sides of the mean balance out. This is the case of a symmetric distribution, but can also be true for an asymmetric distribution where one tail is long and thin, and the other is short but fat.

Below you can see the three types of distribution, namely:

1. Positive skew (Asymmetrical distribution) - The distribution of dataset values is towards the left side or, the curve is leaning to the left and the right side tail is longer.  
2. Symmetrical distribution - The mean, median and mode are all in the middle of the distribution.
3. Negative skew (Asymmetrical distribution) - The distribution of data values is towards the right side or, the curve is leaning to the right and the left side tail is longer.  

Source: Wikipedia