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# scipy.stats.mstats.trimmed_stde

`scipy.stats.mstats.``trimmed_stde`(a, limits=(0.10000000000000001, 0.10000000000000001), inclusive=(1, 1), axis=None)

  Returns the standard error of the trimmed mean of the data along the given axis. Parameters ———- a : sequence

  > Input array

  limits : {(0.1,0.1), tuple of float} optional
    tuple (lower percentage, upper percentage) to cut on each side of the array, with respect to the number of unmasked data. Noting n the number of unmasked data before trimming, the (n*limits[0])th smallest data and the (n*limits[1])th largest data are masked, and the total number of unmasked data after trimming is n*(1.-sum(limits)) In each case, the value of one limit can be set to None to indicate an open interval. If limits is None, no trimming is performed
  inclusive : {(True, True) tuple} optional
    Tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False).
  axis : {None, integer}, optional
    Axis along which to trim.

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