`scipy.stats._binomtest:binomtest`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_binomtest.py:182

Signature

def binomtest(k, n, p = 0.5, alternative = two-sided)

Summary

Perform a test that the probability of success is p.

Extended Summary

The binomial test ^[1] is a test of the null hypothesis that the probability of success in a Bernoulli experiment is p.

Details of the test can be found in many texts on statistics, such as section 24.5 of ^[2].

The documentation is written as though the function accepts and returns Python scalars, but the function is vectorized to work elementwise with NumPy arrays.

Parameters

k : int: The number of successes.
n : int: The number of trials.
p : float, optional: The hypothesized probability of success, i.e. the expected proportion of successes. The value must be in the interval 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'.

Returns

result : `~scipy.stats._result_classes.BinomTestResult` instance

The return value is an object with the following attributes:

k: The number of successes (copied from binomtest input).
n: The number of trials (copied from binomtest input).
alternative: Indicates the alternative hypothesis specified in the input to binomtest. It will be one of 'two-sided', 'greater', or 'less'.
statistic: The estimate of the proportion of successes.
pvalue: The p-value of the hypothesis test.

The object has the following methods:

proportion_ci(confidence_level=0.95, method='exact') :: Compute the confidence interval for statistic.

Notes

Examples

from scipy.stats import binomtest

✓

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:

result = binomtest(3, n=15, p=0.1, alternative='greater')

✓

result.pvalue

✗

np.float64(0.18406106910639106)

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%. The test statistic is equal to the estimated proportion, which is simply ``3/15``:

result.statistic

✗

np.float64(0.2)

We can use the `proportion_ci()` method of the result to compute the confidence interval of the estimate:

result.proportion_ci(confidence_level=0.95)

✗

ConfidenceInterval(low=np.float64(0.056846867590246805), high=np.float64(1.0))

Aliases

scipy.stats.binomtest