# scipy.stats.binom_test¶

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.

Parameters: 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

References

Examples

>>> 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')
0.18406106910639114


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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