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Module « scipy.stats »

Fonction binomtest - module scipy.stats

Signature de la fonction binomtest

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

Description

help(scipy.stats.binomtest)

Perform a test that the probability of success is p.

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]_.

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 : int
        The number of successes (copied from `binomtest` input).
    n : int
        The number of trials (copied from `binomtest` input).
    alternative : str
        Indicates the alternative hypothesis specified in the input
        to `binomtest`.  It will be one of ``'two-sided'``, ``'greater'``,
        or ``'less'``.
    statistic : float
        The estimate of the proportion of successes.
    pvalue : float
        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
-----
.. versionadded:: 1.7.0

References
----------
.. [1] Binomial test, https://en.wikipedia.org/wiki/Binomial_test
.. [2] Jerrold H. Zar, Biostatistical Analysis (fifth edition),
       Prentice Hall, Upper Saddle River, New Jersey USA (2010)

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
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%.

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

>>> result.statistic
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=0.05684686759024681, high=1.0)



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