scipy.stats._result_classes.TukeyHSDResult.confidence_interval#

TukeyHSDResult.confidence_interval(confidence_level=0.95)[source]#

Compute the confidence interval for the specified confidence level.

Parameters:

confidence_levelfloat, optional: Confidence level for the computed confidence interval of the estimated proportion. Default is .95.

Returns:

ciConfidenceInterval object: The object has attributes low and high that hold the lower and upper bounds of the confidence intervals for each comparison. The high and low values are accessible for each comparison at index (i, j) between groups i and j.

References

[1]

NIST/SEMATECH e-Handbook of Statistical Methods, “7.4.7.1. Tukey’s Method.” https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm, 28 November 2020.

Examples

>>> from scipy.stats import tukey_hsd
>>> group0 = [24.5, 23.5, 26.4, 27.1, 29.9]
>>> group1 = [28.4, 34.2, 29.5, 32.2, 30.1]
>>> group2 = [26.1, 28.3, 24.3, 26.2, 27.8]
>>> result = tukey_hsd(group0, group1, group2)
>>> ci = result.confidence_interval()
>>> ci.low
array([[-3.649159, -8.249159, -3.909159],
       [ 0.950841, -3.649159,  0.690841],
       [-3.389159, -7.989159, -3.649159]])
>>> ci.high
array([[ 3.649159, -0.950841,  3.389159],
       [ 8.249159,  3.649159,  7.989159],
       [ 3.909159, -0.690841,  3.649159]])