statsmodels.stats.proportion.proportions_ztest

statsmodels.stats.proportion.proportions_ztest(count, nobs, value=None, alternative='twosided', prop_var=False)
[source]

Test for proportions based on normal (z) test
Parameters: 

count (integer or array_like) – the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample

nobs (integer or arraylike) – the number of trials or observations, with the same length as count.

value (float, array_like or None, optional) – This is the value of the null hypothesis equal to the proportion in the case of a one sample test. In the case of a twosample test, the null hypothesis is that prop[0]  prop[1] = value, where prop is the proportion in the two samples. If not provided value = 0 and the null is prop[0] = prop[1]

alternative (string in ['twosided', 'smaller', 'larger']) – The alternative hypothesis can be either twosided or one of the one sided tests, smaller means that the alternative hypothesis is
prop < value and larger means prop > value . In the two sample test, smaller means that the alternative hypothesis is p1 < p2 and larger means p1 > p2 where p1 is the proportion of the first sample and p2 of the second one. 
prop_var (False or float in (0, 1)) – If prop_var is false, then the variance of the proportion estimate is calculated based on the sample proportion. Alternatively, a proportion can be specified to calculate this variance. Common use case is to use the proportion under the Null hypothesis to specify the variance of the proportion estimate.

Returns: 

zstat (float) – test statistic for the ztest

pvalue (float) – pvalue for the ztest

Examples
>>> count = 5
>>> nobs = 83
>>> value = .05
>>> stat, pval = proportions_ztest(count, nobs, value)
>>> print('{0:0.3f}'.format(pval))
0.695
>>> import numpy as np
>>> from statsmodels.stats.proportion import proportions_ztest
>>> count = np.array([5, 12])
>>> nobs = np.array([83, 99])
>>> stat, pval = proportions_ztest(counts, nobs)
>>> print('{0:0.3f}'.format(pval))
0.159
Notes
This uses a simple normal test for proportions. It should be the same as running the mean ztest on the data encoded 1 for event and 0 for no event so that the sum corresponds to the count.
In the one and two sample cases with twosided alternative, this test produces the same pvalue as proportions_chisquare
, since the chisquare is the distribution of the square of a standard normal distribution.