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scipy.stats._result_classes.TukeyHSDResult.

# confidence_interval

TukeyHSDResult.confidence_interval(confidence_level=0.95)

  Compute the confidence interval for the specified confidence level.

  Parameters:
    confidence_level float, optional

      Confidence level for the computed confidence interval of the estimated proportion. Default is .95.

  Returns:
    ci `ConfidenceInterval` 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.

  [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: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]])