Statistical Science

Inference from Randomized (Factorial) Experiments

R. A. Bailey

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Abstract

This is a contribution to the discussion of the interesting paper by Ding [Statist. Sci. 32 (2017) 331–345], which contrasts approaches attributed to Neyman and Fisher. I believe that Fisher’s usual assumption was unit-treatment additivity, rather than the “sharp null hypothesis” attributed to him. Fisher also developed the notion of interaction in factorial experiments. His explanation leads directly to the concept of marginality, which is essential for the interpretation of data from any factorial experiment.

Article information

Source
Statist. Sci., Volume 32, Number 3 (2017), 352-355.

Dates
First available in Project Euclid: 1 September 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1504253119

Digital Object Identifier
doi:10.1214/16-STS600

Mathematical Reviews number (MathSciNet)
MR3695998

Keywords
Factorial design marginality randomisation unit-treatment additivity

Citation

Bailey, R. A. Inference from Randomized (Factorial) Experiments. Statist. Sci. 32 (2017), no. 3, 352--355. doi:10.1214/16-STS600. https://projecteuclid.org/euclid.ss/1504253119


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See also

  • Main article: A Paradox from Randomization-Based Causal Inference.