Neyman's test of goodness of fit

Purpose: To test whether 2 distributions are equal.

Null hypothesis: Observed distribution = Expected distribution

Alternate hypothesis: Observed distribution ≠ Expected distribution

Code:

>>> from scipy import stats
>>> observed = [16, 18, 16, 14, 12, 12]
>>> expected = [16, 16, 16, 16, 16, 8]
>>> result = stats.power_divergence(observed, expected, lambda_="neyman")
>>> print("statistic = %.2f" % result.statistic)
statistic = 3.17
>>> print("p-value = %.2f" % result.pvalue)
p-value = 0.67

Reference:

1. Neyman J. 1949. Contributions to the theory of the X2 test. Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, 239–273.