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scipy.stats.

# binomtest

scipy.stats.binomtest(k, n, p=0.5, alternative='two-sided')

  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:
    kint

      The number of successes.

    nint

      The number of trials.

    pfloat, 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 `BinomTestResult` instance

      The return value is an object with the following attributes:

      kint

        The number of successes (copied from `binomtest` input).

      nint

        The number of trials (copied from `binomtest` input).

      alternativestr

        Indicates the alternative hypothesis specified in the input to `binomtest`. It will be one of `'two-sided'`, `'greater'`, or `'less'`.

      statisticfloat

        The estimate of the proportion of successes.

      pvaluefloat

        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

  Added in version 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)