G test of independence

Purpose: To test whether 2 categorical variables (defining the rows and columns) are independent

Null hypothesis: There is no relationship between between the 2 categorical variables; that is, the 2 categorical variables are not independent.

Alternate hypothesis: There is a relationship between the 2 categorical variables; that is, the 2 categorical variables are not independent.

Code:

>>> from scipy import stats
>>> table = [[77, 404],
...          [16, 122]]
>>> result = stats.chi2_contingency(table, lambda_="log-likelihood")
>>> print("G = %.3f" % result[0])
G = 1.368
>>> print("p-value = %.3f" % result[1])
p-value = 0.242

Reference:

1. Cressie N, Read TRC. 1984. Multinomial Goodness-of-Fit Tests. Journal of the Royal Statistical Society Series B 46(3), 440-464.