Skewness (this term was first used by Pearson, 1895) measures the deviation of the distribution from symmetry. If the skewness is clearly different from 0, then that distribution is asymmetrical, while normal distributions are perfectly symmetrical.

If the skewness is negative, this suggests that the skew of the distribution is to the left; the distribution has a heavy tail to the left. A positive value indicates a right skew, which implies that there is a heavy tail to the right. The mean is less than the median for a left-skewed distribution; conversely the mean is greater than the median for a right-skewed distribution.

Skewness = n*M3 /[(n-1)*(n-2)*s3]

where

M3 |
is equal to: |

s3 |
is the standard deviation (sigma) raised to the third power |

n |
is the valid number of cases. |

See also, Descriptive Statistics Overview.