Skewness is a measure of the degree of asymmetry of a distribution. If the left tail (tail at small end of the distribution) is more pronounced
than the right tail (tail at the large end of the distribution), the function is
said to have negative skewness. If the reverse is true,
it has positive skewness. If the two are equal, it has
zero skewness.

Several types of skewness are defined, the terminology and notation of which are unfortunately rather confusing. "The" skewness of a distribution is defined to be

(1)

where is the th central moment. The notation
is due to Karl Pearson, but the
notations
(Kenney and Keeping 1951, p. 27; Kenney and Keeping 1962, p. 99) and (due to R. A. Fisher)
are also encountered (Kenney and Keeping 1951, p. 27; Kenney and Keeping 1962,
p. 99; Abramowitz and Stegun 1972, p. 928). Abramowitz and Stegun (1972,
p. 928) also confusingly refer to both and as "skewness." Skewness is implemented
in the Wolfram Language as Skewness[dist].