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Keywords:

  • binomial proportion;
  • hypothesis testing;
  • Lachenbruch test;
  • mixed models;
  • repeated measures;
  • strong null hypothesis

Standard statistical theory teaches us that once the null and alternative hypotheses have been defined for a parameter, the choice of the statistical test is clear. Standard theory does not teach us how to choose the null or alternative hypothesis appropriate to the scientific question of interest. Neither does it tell us that in some cases, depending on which alternatives are realistic, we may want to define our null hypothesis differently. Problems in statistical practice are frequently not as pristinely summarized as the classic theory in our textbooks. In this article, we present examples in statistical hypothesis testing in which seemingly simple choices are in fact rich with nuance that, when given full consideration, make the choice of the right hypothesis test much less straightforward. Published 2012. This article is a US Government work and is in the public domain in the USA.