Randomization-based tests for "no treatment effects"

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)349-351
Number of pages3
JournalStatistical Science
Volume32
Issue number3
DOIs
StatePublished - 2017

Keywords

  • Fisher's randomization test
  • Neyman's randomization test
  • Treatment effect
  • Wilcoxon-Mann-Whitney rank sum test

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

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