Test normality using Anderson-DarlingΒΆ

In this example we are going to evaluate whether a sample follows a normal distribution.

[1]:

from __future__ import print_function
import openturns as ot

[2]:

# Generate a sample
sample = ot.Normal().getSample(200)

[3]:

# Test normality
test_result = ot.NormalityTest.AndersonDarlingNormal(sample)
print('Component is normal?', test_result.getBinaryQualityMeasure(),
      'p-value=%.6g' % test_result.getPValue(),
      'threshold=%.6g' % test_result.getThreshold())

Component is normal? True p-value=0.829806 threshold=0.05

[5]:

# Test succeeded ?
test_result.getBinaryQualityMeasure()

[5]:

True

[7]:

# P-Value
test_result.getPValue()

[7]:

0.8298062868104503