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Skewness, in statistics, is the degree of distortion from the symmetrical bell curve, or normal distribution, in a set of data. Skewness can be negative, positive, zero or undefined. A normal distribution has a skew of zero, while a lognormal distribution, for example, would exhibit some degree of right-skew

• Skewness, in statistics, is the degree of distortion from the symmetrical bell curve in a probability distribution.
• Distributions can exhibit right (positive) skewness or left (negative) skewness to varying degree.
• Investors note skewness when judging a return distribution because it, like kurtosis, considers the extremes of the data set rather than focusing solely on the average.

Skewness can be positive or negative or zero.

• When the values of mean, median and mode are equal, there is no skewness.
• When mean > median > mode, skewness will be positive.
• When mean < median < mode, skewness will be negative.

Characteristic of a good measure of skewness

• It should be a pure number in the sense that its value should be independent of the unit of the series and also degree of variation in the series.
• It should have zero-value, when the distribution is symmetrical.

It should have a meaningful scale of measurement so that we could easily interpret the measured value.

Karl-Pearson’s Method (Personian Coefficient of Skewness)

Karl Pearson has suggested two formulas,

• where the relationship of mean and mode is established;
• where the relationship between mean and median is not established.

Also Read: Pearson Mode Skewness

This calculator calculates the pearson median skewness using mean, mode, standard deviation values.