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But not all data distributions are symmetrical. When data distributions contain extreme scores, these distributions are described as skewed. Skewness describes the extent to which a distribution is asymmetrical about its mean.
A distribution is characterized as skewed when scores are concentrated on one end of the distribution's range with a tail forming on the other end of the range. Because scores can concentrate on either end of a distribution's range, distributions may be positively skewed or negatively skewed.
Positively Skewed Distribution (Skewed to the Right)
A distribution is positively skewed when it there is a clustering of numbers in the low end of the distribution and a tail extends towards the larger numbers (i.e. to the right). In positively skewed distributions, the mean is pulled to the right -- in the direction of the larger scores.
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Mean > Median > Mode
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