#### Previous topic

scipy.stats.mstats.scoreatpercentile

#### Next topic

scipy.stats.mstats.signaltonoise

# scipy.stats.mstats.sem

`scipy.stats.mstats.``sem`(a, axis=0, ddof=1)

  Calculates the standard error of the mean of the input array.

  Also sometimes called standard error of measurement.

  Parameters: | a : array_like An array containing the values for which the standard error is returned. axis : int or None, optional If axis is None, ravel a first. If axis is an integer, this will be the axis over which to operate. Defaults to 0. ddof : int, optional Delta degrees-of-freedom. How many degrees of freedom to adjust for bias in limited samples relative to the population estimate of variance. Defaults to 1.
  Returns: | s : ndarray or float The standard error of the mean in the sample(s), along the input axis.

  Notes

  The default value for ddof changed in scipy 0.15.0 to be consistent with stats.sem as well as with the most common definition used (like in the R documentation).

  Examples

  Find standard error along the first axis:

      >>> from scipy import stats
      >>> a = np.arange(20).reshape(5,4)
      >>> print(stats.mstats.sem(a))
      [2.8284271247461903 2.8284271247461903 2.8284271247461903
       2.8284271247461903]

  Find standard error across the whole array, using n degrees of freedom:

      >>> print(stats.mstats.sem(a, axis=None, ddof=0))
      1.2893796958227628