# scipy.stats.jarque_bera

scipy.stats.jarque_bera(x)

  Perform the Jarque-Bera goodness of fit test on sample data.

  The Jarque-Bera test tests whether the sample data has the skewness and kurtosis matching a normal distribution.

  Note that this test only works for a large enough number of data samples (>2000) as the test statistic asymptotically has a Chi-squared distribution with 2 degrees of freedom.

  Parameters
    x array_like

      Observations of a random variable.

  Returns
    jb_value float

      The test statistic.

    p float

      The p-value for the hypothesis test.

  References

  1

    Jarque, C. and Bera, A. (1980) “Efficient tests for normality, homoscedasticity and serial independence of regression residuals”, 6 Econometric Letters 255-259.

  Examples

      >>> from scipy import stats
      >>> rng = np.random.default_rng()
      >>> x = rng.normal(0, 1, 100000)
      >>> jarque_bera_test = stats.jarque_bera(x)
      >>> jarque_bera_test
      Jarque_beraResult(statistic=3.3415184718131554, pvalue=0.18810419594996775)
      >>> jarque_bera_test.statistic
      3.3415184718131554
      >>> jarque_bera_test.pvalue
      0.18810419594996775

scipy.stats.combine_pvalues scipy.stats.page_trend_test