%feature("docstring") OT::NormalityTest::AndersonDarlingNormal
"Evaluate whether a sample follows a normal distribution.

Using the Anderson Darling Normal test.

**Available usages**:

    NormalityTest.AndersonDarlingNormal(*sample*)

    NormalityTest.AndersonDarlingNormal(*sample, level*)

Parameters
----------
sample : 2-d sequence of float
    Tested sample.
level : positive float
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
NormalityTest_CramerVonMisesNormal

Notes
-----
The test is used to check whether the sample follows a normal distribution. This
test gives more importance to extreme values.

Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> test_result = ot.NormalityTest.AndersonDarlingNormal(sample)
>>> print(test_result)
class=TestResult name=Unnamed type=AndersonDarlingNormal binaryQualityMeasure=true p-value threshold=0.05 p-value=0.7268 description=[]
"

// ---------------------------------------------------------------------

%feature("docstring") OT::NormalityTest::CramerVonMisesNormal
"Evaluate whether a sample follows a normal distribution.

Using the Cramer Von Mises Normal test.

**Available usages**:

    NormalityTest.CramerVonMisesNormal(*sample*)

    NormalityTest.CramerVonMisesNormal(*sample, level*)

Parameters
----------
sample : 2-d sequence of float
    Tested sample.
level : positive float
    Threshold p-value of the test (= 1 - first type risk), it must be
    :math:`< 1`, equal to 0.95 by default.

Returns
-------
testResult : :class:`~openturns.TestResult`
    Structure containing the result of the test.

See Also
--------
NormalityTest_AndersonDarlingNormal

Notes
-----
The test is used to check whether the sample follows a normal distribution. The 
test concerns the deviation squared and integrated over the entire variation
domain, it often appears to be more robust than the Kolmogorov-Smirnov test.

Examples
--------
>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> test_result = ot.NormalityTest.CramerVonMisesNormal(sample)
>>> print(test_result)
class=TestResult name=Unnamed type=CramerVonMisesNormal binaryQualityMeasure=true p-value threshold=0.05 p-value=0.682524 description=[]
"