parameters: (a, axis=0)
return_statement: return NormaltestResult(k2, distributions.chi2.sf(k2, 2))
docstring: Tests whether a sample differs from a normal distribution.
  
      Parameters
      ----------
      a : array_like
          The array containing the data to be tested.
      axis : int or None, optional
          Axis along which to compute test. Default is 0. If None,
          compute over the whole array `a`.
  
      Returns
      -------
      statistic : float or array
          ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
          ``k`` is the z-score returned by `kurtosistest`.
      pvalue : float or array
         A 2-sided chi squared probability for the hypothesis test.
  
      Notes
      -----
      For more details about `normaltest`, see `stats.normaltest`.
docstring_summary: Tests whether a sample differs from a normal distribution.
docstring_tokens: Tests whether a sample differs from a normal distribution .
function: def normaltest(a, axis=0):
      """
      Tests whether a sample differs from a normal distribution.
  
      Parameters
      ----------
      a : array_like
          The array containing the data to be tested.
      axis : int or None, optional
          Axis along which to compute test. Default is 0. If None,
          compute over the whole array `a`.
  
      Returns
      -------
      statistic : float or array
          ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
          ``k`` is the z-score returned by `kurtosistest`.
      pvalue : float or array
         A 2-sided chi squared probability for the hypothesis test.
  
      Notes
      -----
      For more details about `normaltest`, see `stats.normaltest`.
  
      """
      a, axis = _chk_asarray(a, axis)
      s, _ = skewtest(a, axis)
      k, _ = kurtosistest(a, axis)
      k2 = s*s + k*k
  
      return NormaltestResult(k2, distributions.chi2.sf(k2, 2))
function_tokens: def normaltest ( a , axis = 0 ) : a , axis = _chk_asarray ( a , axis ) s , _ = skewtest ( a , axis ) k , _ = kurtosistest ( a , axis ) k2 = s * s + k * k return NormaltestResult ( k2 , distributions . chi2 . sf ( k2 , 2 ) )