Skewed
Skewness in probability theory and statistics is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. Similarly to kurtosis, it provides insights into shape-related characteristics of a distribution. The skewness value can be positive, zero, negative, or undefined.
For a unimodal distribution (a distribution with a single peak), negative skew commonly indicates that the 'tail' is on the left side of the distribution, and positive skew indicates that the tail is on the right. In cases where one tail is long but the other tail is thick, skewness does not obey a simple rule. For example, a zero value in skewness means that the tails on both sides of the mean balance out overall; this is the case for a symmetric distribution but can also be true for an asymmetric distribution where one tail is long and thin, and the other is short but thick. Thus, the symmetry of a distribution cannot be inferred using only its skewness; the distribution shape must be taken into account.
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