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

Perform a test that the probability of success is p.

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

x : integer or array_like

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

n : integer

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

p : float, 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’.

p-value : float

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%.

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