scipy.stats.normaltest#
- scipy.stats.normaltest(a, axis=0, nan_policy='propagate')[source]#
Test whether a sample differs from a normal distribution.
This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D’Agostino and Pearson’s [1], [2] test that combines skew and kurtosis to produce an omnibus test of normality.
- Parameters
- aarray_like
The array containing the sample to be tested.
- axisint or None, optional
Axis along which to compute test. Default is 0. If None, compute over the whole array a.
- nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional
Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):
‘propagate’: returns nan
‘raise’: throws an error
‘omit’: performs the calculations ignoring nan values
- Returns
- statisticfloat or array
s^2 + k^2
, wheres
is the z-score returned byskewtest
andk
is the z-score returned bykurtosistest
.- pvaluefloat or array
A 2-sided chi squared probability for the hypothesis test.
References
- 1
D’Agostino, R. B. (1971), “An omnibus test of normality for moderate and large sample size”, Biometrika, 58, 341-348
- 2
D’Agostino, R. and Pearson, E. S. (1973), “Tests for departure from normality”, Biometrika, 60, 613-622
Examples
>>> from scipy import stats >>> rng = np.random.default_rng() >>> pts = 1000 >>> a = rng.normal(0, 1, size=pts) >>> b = rng.normal(2, 1, size=pts) >>> x = np.concatenate((a, b)) >>> k2, p = stats.normaltest(x) >>> alpha = 1e-3 >>> print("p = {:g}".format(p)) p = 8.4713e-19 >>> if p < alpha: # null hypothesis: x comes from a normal distribution ... print("The null hypothesis can be rejected") ... else: ... print("The null hypothesis cannot be rejected") The null hypothesis can be rejected