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Alphas, betas and skewy distributions: two ways of getting the wrong answer

By Peter Fayers

Abstract

Although many parametric statistical tests are considered to be robust, as recently shown in Methodologist’s Corner, it still pays to be circumspect about the assumptions underlying statistical tests. In this paper I show that robustness mainly refers to α, the type-I error. If the underlying distribution of data is ignored there can be a major penalty in terms of the β, the type-II error, representing a large increase in false negative rate or, equivalently, a severe loss of power of the test

Topics: Invited Papers on Methodology
Publisher: Springer Netherlands
OAI identifier: oai:pubmedcentral.nih.gov:3139856
Provided by: PubMed Central

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Citations

  1. (2005). Introduction to Robust Estimation and Hypothesis Testing (2nd ed.).
  2. (2010). Likert scales, levels of measurement and the ‘‘laws’’ of statistics.