scipy.stats.binom_test(x, n=None, p=0.5, alternative='two-sided')[source]#

Perform a test that the probability of success is p.

Note: binom_test is deprecated; it is recommended that binomtest be used instead.

This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is p.

xint or array_like

The number of successes, or if x has length 2, it is the number of successes and the number of failures.


The number of trials. This is ignored if x gives both the number of successes and failures.

pfloat, optional

The hypothesized probability of success. 0 <= p <= 1. The default value is p = 0.5.

alternative{‘two-sided’, ‘greater’, ‘less’}, optional

Indicates the alternative hypothesis. The default value is ‘two-sided’.


The p-value of the hypothesis test.




>>> from scipy import stats

A car manufacturer claims that no more than 10% of their cars are unsafe. 15 cars are inspected for safety, 3 were found to be unsafe. Test the manufacturer’s claim:

>>> stats.binom_test(3, n=15, p=0.1, alternative='greater')

The null hypothesis cannot be rejected at the 5% level of significance because the returned p-value is greater than the critical value of 5%.