scipy.stats.

tiecorrect#

scipy.stats.tiecorrect(rankvals)[source]#

Tie correction factor for Mann-Whitney U and Kruskal-Wallis H tests.

Parameters:

rankvalsarray_like: A 1-D sequence of ranks. Typically this will be the array returned by rankdata.

Returns:

factorfloat: Correction factor for U or H.

See also

rankdata: Assign ranks to the data
mannwhitneyu: Mann-Whitney rank test
kruskal: Kruskal-Wallis H test

Notes

Array API Standard Support

tiecorrect 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	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

References

[1]

Siegel, S. (1956) Nonparametric Statistics for the Behavioral Sciences. New York: McGraw-Hill.

Examples

>>> from scipy.stats import tiecorrect, rankdata
>>> tiecorrect([1, 2.5, 2.5, 4])
0.9
>>> ranks = rankdata([1, 3, 2, 4, 5, 7, 2, 8, 4])
>>> ranks
array([ 1. ,  4. ,  2.5,  5.5,  7. ,  8. ,  2.5,  9. ,  5.5])
>>> tiecorrect(ranks)
0.9833333333333333