scikit_posthocs.test_median

scikit_posthocs.test_median(data: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str] | DataFrame, val_col: str | None = None, group_col: str | None = None, correction: bool = False, sort: bool = False) → tuple[float, float, int]

Brown-Mood median test.

Tests the null hypothesis that all groups share a common population median, against the alternative that at least one differs [1].

Parameters:

  • data (Union[List, numpy.ndarray, DataFrame]) – An array, any object exposing the array interface or a pandas DataFrame with data values.

  • val_col (str = None) – Name of a DataFrame column that contains dependent variable values (test or response variable). Values should have a non-nominal scale. Must be specified if data is a pandas DataFrame object.

  • group_col (str = None) – Name of a DataFrame column that contains independent variable values (grouping or predictor variable). Must be specified if data is a pandas DataFrame object.

  • correction (bool = False) – Whether to apply Yates’ continuity correction in the underlying chi-squared test.

  • sort (bool = False) – If True, sort data by group_col.

Returns:

P value, chi-squared statistic, and degrees of freedom.

Return type:

tuple[float, float, int]

Notes

Observations are classified as above or at-or-below the grand median (computed once, from the pooled sample) and compared across groups with Pearson’s chi-squared test of independence.

References

Examples

>>> import scikit_posthocs as sp
>>> x = [[1,2,3,5,1], [12,31,54,62,12], [10,12,6,74,11]]
>>> sp.test_median(x)