spectral_connectivity.statistics.adjust_for_multiple_comparisons#

adjust_for_multiple_comparisons(p_values: ndarray[tuple[int, ...], dtype[floating]], alpha: float = 0.05, method: Literal['Benjamini_Hochberg_procedure', 'Bonferroni_correction'] = 'Benjamini_Hochberg_procedure') → ndarray[tuple[int, ...], dtype[bool]][source]#

Apply multiple comparison correction to p-values.

Wrapper function that applies the specified multiple comparison correction method to control either false discovery rate or family-wise error rate.

Parameters:

  • p_values (NDArray[floating], shape (...,)) – P-values from statistical tests to be corrected.

  • alpha (float, default=0.05) – Significance threshold for the correction method.

  • method ({"Benjamini_Hochberg_procedure", "Bonferroni_correction"},) – default=”Benjamini_Hochberg_procedure” Multiple comparison correction method to apply.

Returns:

is_significant – Boolean array indicating which tests remain significant after correction.

Return type:

NDArray[bool], shape (…,)

Examples

>>> import numpy as np
>>> p_vals = np.array([0.001, 0.02, 0.04, 0.3, 0.8])
>>> # Using Benjamini-Hochberg (default)
>>> bh_sig = adjust_for_multiple_comparisons(p_vals)
>>> # Using Bonferroni
>>> bonf_sig = adjust_for_multiple_comparisons(
...     p_vals, method="Bonferroni_correction"
... )