statsmodels.stats.proportion.proportions_ztest

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

Test for proportions based on normal (z) test

Parameters
countinteger 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

nobsinteger or array-like

the number of trials or observations, with the same length as count.

valuefloat, 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 two-sample 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]

alternativestring in [‘two-sided’, ‘smaller’, ‘larger’]

The alternative hypothesis can be either two-sided 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_varFalse 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
zstatfloat

test statistic for the z-test

p-valuefloat

p-value for the z-test

Notes

This uses a simple normal test for proportions. It should be the same as running the mean z-test 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 two-sided alternative, this test produces the same p-value as proportions_chisquare, since the chisquare is the distribution of the square of a standard normal distribution.

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(count, nobs)
>>> print('{0:0.3f}'.format(pval))
0.159