scikit_posthocs.outliers_grubbs
- scikit_posthocs.outliers_grubbs(x: List | ndarray, hypo: bool = False, alpha: float = 0.05) ndarray | bool
Grubbs’ Test for Outliers [1]. This is the two-sided version of the test. The null hypothesis implies that there are no outliers in the data set.
- Parameters:
x (Union[List, np.ndarray]) – An array, any object exposing the array interface, containing data to test for an outlier in.
hypo (bool = False) –
Specifies whether to return a bool value of a hypothesis test result. Returns
True
when we can reject the null hypothesis. Otherwise,False
. Available options are:True
: return a hypothesis test resultFalse
: return a filtered array without an outlier (default)
alpha (float = 0.05) – Significance level for a hypothesis test.
- Returns:
Returns a filtered array if alternative hypothesis is true, otherwise an unfiltered array. Returns null hypothesis test result instead of an array if
hypo
argument is set toTrue
.- Return type:
Union[np.ndarray, bool]
Notes
Examples
>>> x = np.array([199.31,199.53,200.19,200.82,201.92,201.95,202.18,245.57]) >>> ph.outliers_grubbs(x) array([ 199.31, 199.53, 200.19, 200.82, 201.92, 201.95, 202.18])