scipy.stats.mstats.kurtosis(a, axis=0, fisher=True, bias=True)

Computes the kurtosis (Fisher or Pearson) of a dataset.

Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution.

If bias is False then the kurtosis is calculated using k statistics to eliminate bias comming from biased moment estimators

Use kurtosistest() to see if result is close enough to normal.


a : array

axis : int or None

fisher : bool

If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0).

bias : bool

If False, then the calculations are corrected for statistical bias.


The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s :

definition and 0 for Pearson’s definition. :


[CRCProbStat2000] section 2.2.25