A Study of the Powers of Several Methods of Multiple Comparisons
- 1 September 1975
- journal article
- research article
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 70 (351a) , 574-583
- https://doi.org/10.1080/01621459.1975.10482474
Abstract
Powers of multiple comparisons procedures are studied for fixed maximal experimentwise levels. Analytical considerations show Tukey-Scheffé methods to have least power, Duncan's to be intermediate, Ryan's most powerful. (Newman-Keuls tests could preserve experimentwise levels only if modified radically and impractically.) Extensive Monte-Carlo trials show these power differences to be small, especially for range statistics. We therefore generally recommend the Tukey technique for its elegant simplicity and existent confidence bounds—its power is little below that of any other method. Simulation was for 3, 4 and 5 treatments: the conclusions might need modification for more treatments.Keywords
This publication has 19 references indexed in Scilit:
- An Evaluation of Ten Pairwise Multiple Comparison Procedures by Monte Carlo MethodsJournal of the American Statistical Association, 1973
- The power function of range and studentized range tests in normal samplesBiometrika, 1972
- Multiple Comparisons of MeansAmerican Educational Research Journal, 1971
- Error rates for multiple comparison methods: Some evidence concerning the frequency of erroneous conclusions.Psychological Bulletin, 1969
- A Procedure for Testing the Homogeneity of All Sets of Means in Analysis of VariancePublished by JSTOR ,1964
- Multiple Range and Multiple F TestsPublished by JSTOR ,1955
- Some recent developments in analysis of varianceCommunications on Pure and Applied Mathematics, 1955
- The use of the „studentized range” in connection with an analysis of varianceEuphytica, 1952
- On Dependent Tests of Significance in the Analysis of VarianceThe Annals of Mathematical Statistics, 1951
- THE DISTRIBUTION OF RANGE IN SAMPLES FROM A NORMAL POPULATION, EXPRESSED IN TERMS OF AN INDEPENDENT ESTIMATE OF STANDARD DEVIATIONBiometrika, 1939