code: def find_outliers(data, treshold=2.58):
      
      outliers = []
      mean = np.mean(data)
      std = np.std(data)
      for i in data:
          if abs(i - mean)/std < treshold:
              outliers.append(False)
          else:
              outliers.append(True)
      outliers = np.array(outliers)
      return (outliers)
docs: Identify outliers (abnormal values) using the standart deviation.
  
      Parameters
      ----------
      data : list or ndarray
          Data array
      treshold : float
          Maximum deviation (in terms of standart deviation). Rule of thumb of a gaussian distribution: 2.58 = rejecting 1%, 2.33 = rejecting 2%, 1.96 = 5% and 1.28 = rejecting 10%.
  
      Returns
      ----------
      outliers : ndarray
          A list of True/False with True being the outliers.
  
      Example
      ----------
      >>> import neurokit as nk
      >>> outliers = nk.find_outliers([1, 2, 1, 5, 666, 4, 1 ,3, 5])
  
      Notes
      ----------
      *Authors*
  
      - `Dominique Makowski <https://dominiquemakowski.github.io/>`_
  
      *Dependencies*
  
      - numpy