def METHOD_NAME(x_org):
    x_full = np.array(x_org[~pd.isnull(x_org)])
    # first scale on raw data
    x = preprocessing.scale(x_full)
    # second scale on log data
    x_log = preprocessing.scale(np.log(x_full - np.min(x_full) + 1))
    # the old approach, let's check how new approach will work
    # original_skew = np.abs(stats.skew(x))
    # log_skew = np.abs(stats.skew(x_log))
    # return log_skew < original_skew
    ########################################################################
    # p is probability of being normal distributions
    k2, p1 = stats.normaltest(x)
    k2, p2 = stats.normaltest(x_log)
    return p2 > p1