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:
- ciConfidenceIntervalobject
- The object has attributes - lowand- highthat 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- iand- j.
 
- ci
 - 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. [2]- P. A. Games and J. F. Howell, “Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study,” Journal of Educational Statistics, vol. 1, no. 2, pp. 113-125, Jun. 1976, doi: https://doi.org/10.3102/10769986001002113. - 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]])