scipy.stats._result_classes.BinomTestResult.proportion_ci#

BinomTestResult.proportion_ci(confidence_level=0.95, method='exact')[source]#

Compute the confidence interval for the estimated proportion.

Parameters

confidence_levelfloat, optional

Confidence level for the computed confidence interval of the estimated proportion. Default is 0.95.

method{‘exact’, ‘wilson’, ‘wilsoncc’}, optional

Selects the method used to compute the confidence interval for the estimate of the proportion:

‘exact’ :: Use the Clopper-Pearson exact method [1].
‘wilson’ :: Wilson’s method, without continuity correction ([2], [3]).
‘wilsoncc’ :: Wilson’s method, with continuity correction ([2], [3]).

Default is 'exact'.

Returns

ciConfidenceInterval object: The object has attributes low and high that hold the lower and upper bounds of the confidence interval.

References

1: C. J. Clopper and E. S. Pearson, The use of confidence or fiducial limits illustrated in the case of the binomial, Biometrika, Vol. 26, No. 4, pp 404-413 (Dec. 1934).
2(1,2): E. B. Wilson, Probable inference, the law of succession, and statistical inference, J. Amer. Stat. Assoc., 22, pp 209-212 (1927).
3(1,2): Robert G. Newcombe, Two-sided confidence intervals for the single proportion: comparison of seven methods, Statistics in Medicine, 17, pp 857-872 (1998).

Examples

>>> from scipy.stats import binomtest
>>> result = binomtest(k=7, n=50, p=0.1)
>>> result.proportion_estimate
0.14
>>> result.proportion_ci()
ConfidenceInterval(low=0.05819170033997342, high=0.26739600249700846)