Anderson-Darling Test

Tests whether a data sample has a Gaussian distribution.

Assumptions

• Observations in each sample are independent and identically distributed (iid).

Hypothesis Formulation

• H0: the sample has a Gaussian distribution.
• H1: the sample does not have a Gaussian distribution.

Code Implementation

# Example of the Anderson-Darling Normality Test
from scipy.stats import anderson
data = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]
result = anderson(data)
print('stat=%.3f' % (result.statistic))
for i in range(len(result.critical_values)):
	sl, cv = result.significance_level[i], result.critical_values[i]
	if result.statistic < cv:
		print('Probably Gaussian at the %.1f level' % (sl))