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Confounded Experiments are Simple, Effficient and Misunderstood

Published online by Cambridge University Press:  03 October 2008

R. Mead
Affiliation:
University of Reading, Department of Applied Statistics, Whiteknights, Reading RG6 2AN, England

Summary

Factorial treatment structure and, in particular, confounded designs are important methods of using experimental resources efficiently. Confounded experiments are not used, and possibly not understood, by experimenters. However, the construction and analysis of confounded designs is logically extremely simple, and can be expressed in terms of simple principles. Modern computing facilities can make confounded designs even more useful.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1984

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References

REFERENCES

Cochran, W. G. & Cox, G. M. (1957). Experimental Design (2nd edn). London: Wiley.Google Scholar
Finney, D. J. (1947). The construction of confounded arrangements. Empire Journal of Experimental Agriculture 15:107112.Google Scholar
Fisher, R. A. (1942). The theory of confounding in factorial experiments in relation to the theory of groups. Annals of Eugenics 11:341353.CrossRefGoogle Scholar
Mead, R. & Cumow, R. N. (1983). Statistical Methods in Agriculture and Experimental Biology. London: Chapman and Hall.CrossRefGoogle Scholar
Yates, F. (1935). Complex experiments. Journal of the Royal Statistical Society, Supplement 2:181247.Google Scholar
Yates, F. (1937). The design and analysis of factorial experiments. Imperial Bureau of Soil Science Technical Communication 35.Google Scholar