## Statistical Science

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

### Inference from Randomized (Factorial) Experiments

#### 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

#### See also

- Main article: A Paradox from Randomization-Based Causal Inference. Digital Object Identifier: doi:10.1214/16-STS571