`scipy.stats._stats_py:friedmanchisquare`

source: /scipy/stats/_stats_py.py :8503

Signature

   def     friedmanchisquare (  *  samples    ,    axis    =  0   ,    nan_policy    =  propagate   ,    keepdims    =  False     )

Summary

Compute the Friedman test for repeated samples.

Extended Summary

The Friedman test tests the null hypothesis that repeated samples of the same individuals have the same distribution. It is often used to test for consistency among samples obtained in different ways. For example, if two sampling techniques are used on the same set of individuals, the Friedman test can be used to determine if the two sampling techniques are consistent.

Parameters

sample1, sample2, sample3... : array_like

Arrays of observations. All of the arrays must have the same number of elements. At least three samples must be given.

axis : int or None, default: 0

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

statistic : float: The test statistic, correcting for ties.
pvalue : float: The associated p-value assuming that the test statistic has a chi squared distribution.

Notes

Due to the assumption that the test statistic has a chi squared distribution, the p-value is only reliable for n > 10 and more than 6 repeated samples.

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.

Array API Standard Support

friedmanchisquare has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ✅                     ✅                   
JAX                   ⚠️ no JIT             ⚠️ no JIT           
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

import numpy as np
rng = np.random.default_rng(seed=18)
x = rng.random((6, 10))
from scipy.stats import friedmanchisquare
res = friedmanchisquare(x[0], x[1], x[2], x[3], x[4], x[5])

✓

res.statistic, res.pvalue

✗

The p-value is less than 0.05; however, as noted above, the results may not be reliable since we have a small number of repeated samples. For a more detailed example, see :ref:`hypothesis_friedmanchisquare`.

Aliases

scipy.stats.friedmanchisquare