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.
Deprecated since version 1.10.0:
binom_test
is deprecated in favour ofbinomtest
and will be removed in Scipy 1.12.0.- Parameters:
- 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.
- nint
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 isp = 0.5
.- alternative{‘two-sided’, ‘greater’, ‘less’}, optional
Indicates the alternative hypothesis. The default value is ‘two-sided’.
- Returns:
- p-valuefloat
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%.