Statistical Science

Randomization-Based Tests for “No Treatment Effects”

EunYi Chung

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Although both Fisher’s and Neyman’s tests are for testing “no treatment effects,” they both test fundamentally different null hypotheses. While Neyman’s null concerns the average casual effect, Fisher’s null focuses on the individual causal effect. When conducting a test, researchers need to understand what is really being tested and what underlying assumptions are being made. If these fundamental issues are not fully appreciated, dubious conclusions regarding causal effects can be made.

Article information

Statist. Sci., Volume 32, Number 3 (2017), 349-351.

First available in Project Euclid: 1 September 2017

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Mathematical Reviews number (MathSciNet)

Fisher’s randomization test Neyman’s randomization test treatment effect Wilcoxon–Mann–Whitney rank sum test


Chung, EunYi. Randomization-Based Tests for “No Treatment Effects”. Statist. Sci. 32 (2017), no. 3, 349--351. doi:10.1214/16-STS590.

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  • Chung, E. and Romano, J. P. (2013). Exact and asymptotically robust permutation tests. Ann. Statist. 41 484–507.
  • Chung, E. and Romano, J. P. (2016). Asymptotically valid and exact permutation tests based on two-sample $U$-statistics. J. Statist. Plann. Inference 168 97–105.
  • Romano, J. P. (1990). On the behavior of randomization tests without a group invariance assumption. J. Amer. Statist. Assoc. 85 686–692.

See also

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