### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »
- Statistical functions for masked arrays (`scipy.stats.mstats`) »

# 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 comming from biased moment estimators

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

  Parameters: | 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.
  Returns: | The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s : definition and 0 for Pearson’s definition. :

  References

  [CRCProbStat2000] section 2.2.25

### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »
- Statistical functions for masked arrays (`scipy.stats.mstats`) »