A closer look at testing the “no-treatment-effect” hypothesis in a comparative experiment

• Autores: Joseph B. Lang
• Localización: Statistical science, ISSN 0883-4237, Vol. 30, Nº. 3, 2015, págs. 352-371
• Idioma: inglés
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• Resumen

  • Standard tests of the “no-treatment-effect” hypothesis for a comparative experiment include permutation tests, the Wilcoxon rank sum test, two-sample t tests, and Fisher-type randomization tests. Practitioners are aware that these procedures test different no-effect hypotheses and are based on different modeling assumptions. However, this awareness is not always, or even usually, accompanied by a clear understanding or appreciation of these differences. Borrowing from the rich literatures on causality and finitepopulation sampling theory, this paper develops a modeling framework that affords answers to several important questions, including: exactly what hypothesis is being tested, what model assumptions are being made, and are there other, perhaps better, approaches to testing a no-effect hypothesis? The framework lends itself to clear descriptions of three main inference approaches:

    process-based, randomization-based, and selection-based. It also promotes careful consideration of model assumptions and targets of inference, and highlights the importance of randomization. Along the way, Fishertype randomization tests are compared to permutation tests and a less wellknown Neyman-type randomization test. A simulation study compares the operating characteristics of the