Pearson

Pearson(firstSample, secondSample, level=0.05)

Test whether two discrete samples are independent.

Refer to Pearson’s correlation test.

Parameters:
firstSample2-d sequence of float

First tested sample, of dimension n \geq 1.

secondSample2-d sequence of float

Second tested sample, of dimension 1.

levelpositive float < 1, optional

Threshold p-value of the test (= first kind risk), it must be < 1, equal to 0.05 by default.

Returns:
testResultTestResult

Structure containing the result of the test.

See also

openturns.HypothesisTest.Spearman

Notes

The Pearson Test is used to check whether two samples which are assumed to form a gaussian vector are independent (based on the evaluation of the linear correlation coefficient).

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distCol = [ot.Normal(), ot.Normal()]
>>> firstSample = ot.Normal().getSample(30)
>>> secondSample = ot.Normal().getSample(30)
>>> test_result = ot.HypothesisTest.Pearson(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=Pearson binaryQualityMeasure=true p-value threshold=0.05 p-value=0.984737 statistic=0.019302 description=[]
>>> independent_samples = test_result.getBinaryQualityMeasure()

Examples using the function

Test independence

Test independence