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# scipy.stats.jarque_bera

scipy.stats.jarque_bera(x, *, axis=None, nan_policy='propagate', keepdims=False)

  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.

    axis int or None, default: None

      If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If `None`, the input will be raveled before computing the statistic.

    nan_policy{‘propagate’, ‘omit’, ‘raise’}

      Defines how to handle input NaNs.

      - `propagate`: if a NaN is present in the axis slice (e.g. row) along which the statistic is computed, the corresponding entry of the output will be NaN.

      - `omit`: NaNs will be omitted when performing the calculation. If insufficient data remains in the axis slice along which the statistic is computed, the corresponding entry of the output will be NaN.

      - `raise`: if a NaN is present, a `ValueError` will be raised.

    keepdims bool, default: False

      If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

  Returns:
    result SignificanceResult

      An object with the following attributes:

      statisticfloat

        The test statistic.

      pvaluefloat

        The p-value for the hypothesis test.

  Notes

  Beginning in SciPy 1.9, `np.matrix` inputs (not recommended for new code) are converted to `np.ndarray` before the calculation is performed. In this case, the output will be a scalar or `np.ndarray` of appropriate shape rather than a 2D `np.matrix`. Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or `np.ndarray` rather than a masked array with `mask=False`.

  References

  [1]

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

  [2]

  Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.

  [3]

  B. Phipson and G. K. Smyth. “Permutation P-values Should Never Be Zero: Calculating Exact P-values When Permutations Are Randomly Drawn.” Statistical Applications in Genetics and Molecular Biology 9.1 (2010).

  [4]

  Panagiotakos, D. B. (2008). The value of p-value in biomedical research. The open cardiovascular medicine journal, 2, 97.

  Examples

  Suppose we wish to infer from measurements whether the weights of adult human males in a medical study are not normally distributed [2]. The weights (lbs) are recorded in the array `x` below.

      >>> import numpy as np
      >>> x = np.array([148, 154, 158, 160, 161, 162, 166, 170, 182, 195, 236])

  The Jarque-Bera test begins by computing a statistic based on the sample skewness and kurtosis.

      >>> from scipy import stats
      >>> res = stats.jarque_bera(x)
      >>> res.statistic
      6.982848237344646

  Because the normal distribution has zero skewness and zero (“excess” or “Fisher”) kurtosis, the value of this statistic tends to be low for samples drawn from a normal distribution.

  The test is performed by comparing the observed value of the statistic against the null distribution: the distribution of statistic values derived under the null hypothesis that the weights were drawn from a normal distribution. For the Jarque-Bera test, the null distribution for very large samples is the chi-squared distribution with two degrees of freedom.

      >>> import matplotlib.pyplot as plt
      >>> dist = stats.chi2(df=2)
      >>> jb_val = np.linspace(0, 11, 100)
      >>> pdf = dist.pdf(jb_val)
      >>> fig, ax = plt.subplots(figsize=(8, 5))
      >>> def jb_plot(ax):  # we'll reuse this
      ...     ax.plot(jb_val, pdf)
      ...     ax.set_title("Jarque-Bera Null Distribution")
      ...     ax.set_xlabel("statistic")
      ...     ax.set_ylabel("probability density")
      >>> jb_plot(ax)
      >>> plt.show()

  The comparison is quantified by the p-value: the proportion of values in the null distribution greater than or equal to the observed value of the statistic.

      >>> fig, ax = plt.subplots(figsize=(8, 5))
      >>> jb_plot(ax)
      >>> pvalue = dist.sf(res.statistic)
      >>> annotation = (f'p-value={pvalue:.6f}\n(shaded area)')
      >>> props = dict(facecolor='black', width=1, headwidth=5, headlength=8)
      >>> _ = ax.annotate(annotation, (7.5, 0.01), (8, 0.05), arrowprops=props)
      >>> i = jb_val >= res.statistic  # indices of more extreme statistic values
      >>> ax.fill_between(jb_val[i], y1=0, y2=pdf[i])
      >>> ax.set_xlim(0, 11)
      >>> ax.set_ylim(0, 0.3)
      >>> plt.show()

      >>> res.pvalue
      0.03045746622458189

  If the p-value is “small” - that is, if there is a low probability of sampling data from a normally distributed population that produces such an extreme value of the statistic - this may be taken as evidence against the null hypothesis in favor of the alternative: the weights were not drawn from a normal distribution. Note that:

  - The inverse is not true; that is, the test is not used to provide evidence for the null hypothesis.

  - The threshold for values that will be considered “small” is a choice that should be made before the data is analyzed [3] with consideration of the risks of both false positives (incorrectly rejecting the null hypothesis) and false negatives (failure to reject a false null hypothesis).

  Note that the chi-squared distribution provides an asymptotic approximation of the null distribution; it is only accurate for samples with many observations. For small samples like ours, `scipy.stats.monte_carlo_test` may provide a more accurate, albeit stochastic, approximation of the exact p-value.

      >>> def statistic(x, axis):
      ...     # underlying calculation of the Jarque Bera statistic
      ...     s = stats.skew(x, axis=axis)
      ...     k = stats.kurtosis(x, axis=axis)
      ...     return x.shape[axis]/6 * (s**2 + k**2/4)
      >>> res = stats.monte_carlo_test(x, stats.norm.rvs, statistic,
      ...                              alternative='greater')
      >>> fig, ax = plt.subplots(figsize=(8, 5))
      >>> jb_plot(ax)
      >>> ax.hist(res.null_distribution, np.linspace(0, 10, 50),
      ...         density=True)
      >>> ax.legend(['aymptotic approximation (many observations)',
      ...            'Monte Carlo approximation (11 observations)'])
      >>> plt.show()

      >>> res.pvalue
      0.0097  # may vary

  Furthermore, despite their stochastic nature, p-values computed in this way can be used to exactly control the rate of false rejections of the null hypothesis [4].