Skewness and Kurtosis

Skewness and kurtosis are terms that describe the shape and symmetry of a distribution of scores. Unless you plan to do inferential statistics on your data set skewness and kurtosis only serve as descriptions of the distribution of your data. Be aware that neither of these measures should be trusted unless you have a large sample size.

Skewness refers to whether the distribution is symmetrical with respect to its dispersion from the mean. If on one side of the mean has extreme scores but the other does not, the distribution is said to be skewed. If the dispersion of scores on either side of the mean are roughly symmetrical (i.e. one is a mirror reflection of the other, the distribution is said to be not skewed.

Click here to see some examples of skewed and non skewed distributions.

Kurtosis refers to the weight of the tails of a distribution. Distributions where a large proportion of the scores are towards the extremes are said to be platykurtic. If, on the other hand, the scores are bunched up near the mean, the distribution is said to be leptokurtic. A normally distributed distribution of scores is said to be mesokurtic.

Click here to see examples of kurtosis.