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# scipy.stats.mstats.kurtosis

`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 coming from biased moment estimators

  Use `kurtosistest` to see if result is close enough to normal.

  Parameters: | a : array data for which the kurtosis is calculated axis : int or None, optional Axis along which the kurtosis is calculated. Default is 0. If None, compute over the whole array a. fisher : bool, optional If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0). bias : bool, optional If False, then the calculations are corrected for statistical bias.
  Returns: | kurtosis : array The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s definition and 0 for Pearson’s definition.

  Notes

  For more details about `kurtosis`, see stats.kurtosis.