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**Highlights of the topic- Skewness **

In the statistical distribution skewness often means asymmetry. Thus when the curve appears skewed or you can say distorted either to the right hand side or the left hand side then skewness is said to be visible. When we talk about the quantification aspect of skewness, it can be quantified so as to define the extent to which a particular distribution varies from the normal distribution. Skewness is highly useful for analyzing probability theory and other statistical concepts.

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**Some interpretations of skewness**

There are different interpretations of skewness and here is a brief idea about them –

**Negative skewness**–

When the distribution appears skewed to the left hand side then in real terms the mean is less than the mode. It denotes a case of negative skewness.

**Positive skewness**–

When the distribution is skewed to the right hand side then the interpretation is that the mean is greater as compared to the mode and it is a case of positive skewness.

Sometimes skewness can be undefined as well depending on case to case. Thus skewness is different from the case of perfect normal distribution because in that the tails on both the sides of the curve are just like the mirror replicas of each other, which actually represents that mean and mode are equal.

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