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New and Traditional Methods for the Analysis of Unreplicated Experiments
Author(s) -
Payne Roger W.
Publication year - 2006
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2006.04.0273
Subject(s) - replication (statistics) , computer science , field (mathematics) , contrast (vision) , econometrics , statistics , multiple comparisons problem , biology , mathematics , artificial intelligence , pure mathematics
This paper reviews some traditional and more recent methods for analyzing unreplicated experiments. Such experiments have presented a challenge to statisticians throughout their involvement in agricultural research. At Rothamsted this began in 1919, when R.A. Fisher was appointed to analyze the accumulated data from the classical field experiments. Fisher's experiences with the classicals, which had virtually no replication, must have contributed to his inclusion of replication as one of the key features of a well‐designed experiment. Nevertheless, Fisher made good use of Rothamsted's data, for example in his study of the influence of rainfall on yields from the Broadbalk. He also devised the randomization test, which can be used to analyze unreplicated data. More recently, Broadbalk has also been used to study climate change and sustainability. Newer developments have been concerned to find alternatives to use, instead of blocking, to take account of the spatial variation within an experiment. The resulting methods for modeling spatial correlations have allowed experimenters to obtain more precise estimates of treatment effects—or to decrease numbers of replicates—and they can also provide reliable analyses of unreplicated treatments.