Statistical Science

On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9

Jerzy Splawa-Neyman, D. M. Dabrowska, and T. P. Speed

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Abstract

In the portion of the paper translated here, Neyman introduces a model for the analysis of field experiments conducted for the purpose of comparing a number of crop varieties, which makes use of a double-indexed array of unknown potential yields, one index corresponding to varieties and the other to plots. The yield corresponding to only one variety will be observed on any given plot, but through an urn model embodying sampling without replacement from this doubly indexed array, Neyman obtains a formula for the variance of the difference between the averages of the observed yields of two varieties. This variance involves the variance over all plots of the potential yields and the correlation coefficient $r$ between the potential yields of the two varieties on the same plot. Since it is impossible to estimate $r$ directly, Neyman advises taking $r = 1$, observing that in practice this may lead to using too large an estimated standard deviation, when comparing two variety means.

Article information

Source
Statist. Sci., Volume 5, Number 4 (1990), 465-472.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.ss/1177012031

Digital Object Identifier
doi:10.1214/ss/1177012031

Mathematical Reviews number (MathSciNet)
MR1092986

Zentralblatt MATH identifier
0955.01560

JSTOR
links.jstor.org

Keywords
Field experiment varieties unknown potential yields urn model sampling without replacement correlation

Citation

Splawa-Neyman, Jerzy; Dabrowska, D. M.; Speed, T. P. On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9. Statist. Sci. 5 (1990), no. 4, 465--472. doi:10.1214/ss/1177012031. https://projecteuclid.org/euclid.ss/1177012031


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See also

  • See Comment: Donald B. Rubin. [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies. Statist. Sci., Volume 5, Number 4 (1990), 472--480.