A randomization-based perspective on analysis of variance: a test statistic robust to treatment effect heterogeneity

Peng Ding and Tirthankar Dasgupta

Biometrika, 2018, vol. 105, issue 1, 45-56

Abstract: Summary Fisher randomization tests for Neyman’s null hypothesis of no average treatment effect are considered in a finite-population setting associated with completely randomized experiments involving more than two treatments. The consequences of using the $F$ statistic to conduct such a test are examined, and we argue that under treatment effect heterogeneity, use of the $F$ statistic in the Fisher randomization test can severely inflate the Type I error under Neyman’s null hypothesis. We propose to use an alternative test statistic, derive its asymptotic distributions under Fisher’s and Neyman’s null hypotheses, and demonstrate its advantages through simulations.

Keywords: Additivity; Fisher randomization test; Null hypothesis; One-way layout (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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