Differentiate between Negative, Positive and Symmetric skewed.

 Differentiate between Negative, Positive and Symmetric skewed.


Skewness is a measure of the asymmetry of a distribution. It indicates the extent to which a dataset deviates from a symmetric distribution. Skewness can be categorized into three types: negative skewness, positive skewness, and symmetric skewness. Let's differentiate between them:


Negative Skewness:

Also known as left-skewed or left-tailed distribution.

In a negatively skewed distribution, the tail on the left side of the distribution is longer or fatter than the right side.

The mean tends to be less than the median and the mode.

The left tail of the distribution is stretched or pulled in the negative direction.

Negative skewness typically occurs when there are outliers on the lower end of the distribution that pull the mean downward.


Positive Skewness:

Also known as right-skewed or right-tailed distribution.

In a positively skewed distribution, the tail on the right side of the distribution is longer or fatter than the left side.

The mean tends to be greater than the median and the mode.

The right tail of the distribution is stretched or pulled in the positive direction.

Positive skewness often occurs when there are outliers on the higher end of the distribution that pull the mean upward.


Symmetric Skewness:

A symmetric distribution has no skewness.

In a symmetric distribution, the left and right sides of the distribution mirror each other.

The mean, median, and mode are approximately equal in a symmetric distribution.

The data is evenly distributed around the central point without any significant tailing.

To summarize, negative skewness indicates a longer or fatter left tail, positive skewness indicates a longer or fatter right tail, and symmetric skewness indicates a balanced and symmetric distribution without any skewness.






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