Noun
(statistics) A measure of "heaviness of the tails" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution.
(statistics) Excess kurtosis: the difference between a given distribution's kurtosis and the kurtosis of a normal distribution.
Source: en.wiktionary.orgAlternatively, given two subpopulations with the same mean and different standard deviations, the overall population will exhibit high kurtosis, with a sharper peak and heavier tails (and correspondingly shallower shoulders) than a single distribution. Source: Internet
D'Agostino's K-squared test is a goodness-of-fit normality test based on a combination of the sample skewness and sample kurtosis, as is the Jarque–Bera test for normality. Source: Internet
D'Agostino's K-squared test is a goodness-of-fit normality test based on sample skewness and sample kurtosis. Source: Internet
Excess kurtosis The excess kurtosis is defined as kurtosis minus 3. There are 3 distinct regimes as described below. Source: Internet
For the kurtosis to exist, we require m > 5/2. Source: Internet
Interpretation The exact interpretation of the Pearson measure of kurtosis (or excess kurtosis) used to be disputed, but is now settled. Source: Internet