spectral_connectivity.statistics.Bonferroni_correction#

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

Control family-wise error rate using Bonferroni correction.

Corrects for multiple comparisons by dividing the significance level by the number of tests. This is a conservative method that controls the family-wise error rate.

Parameters:

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

  • alpha (float, default=0.05) – Critical threshold for significance testing.

Returns:

is_significant – Boolean array indicating significant tests after Bonferroni 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])
>>> significant = Bonferroni_correction(p_vals, alpha=0.05)
>>> significant
array([ True, False, False, False, False])