binomtest#
- scipy.stats.binomtest(k, n, p=0.5, alternative='two-sided')[source]#
- 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:
- resultBinomTestResultinstance
- The return value is an object with the following attributes: - kint
- The number of successes (copied from - binomtestinput).
- nint
- The number of trials (copied from - binomtestinput).
- 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.
 
 
- result
 - 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)