The Emperor’s new tests

Subscribe to email list

Please select the email list(s) to which you wish to subscribe.

User menu

You are here

The Emperor’s new tests

TitleThe Emperor’s new tests
Publication TypeJournal Article
Year of Publication1999
AuthorsPerlman, MD, WU, LANG
JournalStatistical Science
Date Publishednov
ISSN0883-4237, 2168-8745
Keywordsa-admissibility, bioequivalence problem, d-admissibility, Fisher-Neyman debate, Hypothesis test, likelihood ratio test, multiple endpoints in clinical trials, multivariate one-sided alternatives, order-restricted hypotheses, power, significance test, size test, test for qualitative interactions, unbiased test
AbstractIn the past two decades, striking examples of allegedly inferior likelihood ratio tests (LRT) have appeared in the statistical literature. These examples, which arise in multiparameter hypothesis testing problems, have several common features. In each case the null hypothesis is composite, the size LRT is not similar and hence biased, and competing size tests can be constructed that are less biased, or even unbiased, and that dominate the LRT in the sense of being everywhere more powerful. It is therefore asserted that in these examples and, by implication, many other testing problems, the LR criterion produces ‘‘inferior,’’ ‘‘deficient,’’ ‘‘ undesirable,’’ or ‘‘flawed’’ statistical procedures. This message, which appears to be proliferating, is wrong. In each example it is the allegedly superior test that is flawed, not the LRT. At worst, the ‘‘superior’’ tests provide unwarranted and inappropriate inferences and have been deemed scientifically unacceptable by applied statisticians. This reinforces the well-documented but oft-neglected fact that the Neyman-Pearson theory desideratum of a more or most powerful size test may be scientifically inappropriate; the same is true for the criteria of unbiasedness and -admissibility. Although the LR criterion is not infallible, we believe that it remains a generally reasonable first option for non-Bayesian parametric hypothesis-testing problems.