pingouin.anderson#

pingouin.anderson(*args, dist='norm')[source]#

Anderson-Darling test of distribution.

The Anderson-Darling test tests the null hypothesis that a sample is drawn from a population that follows a particular distribution. For the Anderson-Darling test, the critical values depend on which distribution is being tested against.

This function is a wrapper around scipy.stats.anderson().

Parameters:
sample1, sample2,…array_like

Array of sample data. They may be of different lengths.

diststring

The type of distribution to test against. The default is ‘norm’. Must be one of ‘norm’, ‘expon’, ‘logistic’, ‘gumbel’.

Returns:
from_distboolean

A boolean indicating if the data comes from the tested distribution (True) or not (False).

sig_levelfloat

The significance levels for the corresponding critical values, in %. See scipy.stats.anderson() for more details.

Examples

  1. Test that an array comes from a normal distribution

>>> from pingouin import anderson
>>> import numpy as np
>>> np.random.seed(42)
>>> x = np.random.normal(size=100)
>>> y = np.random.normal(size=10000)
>>> z = np.random.random(1000)
>>> anderson(x)
(True, 15.0)

  1. Test that multiple arrays comes from the normal distribution

>>> anderson(x, y, z)
(array([ True,  True, False]), array([15., 15.,  1.]))

  1. Test that an array comes from the exponential distribution

>>> x = np.random.exponential(size=1000)
>>> anderson(x, dist="expon")
(True, 15.0)