A Nonrandomized, Nonconservative Version of the Fisher Exact Test
Title:
A Nonrandomized, Nonconservative Version of the Fisher Exact Test
Author:
van der Meulen, Egbert A.
Appeared in:
Communications in statistics
Paging:
Volume 37 (2008) nr. 5 pages 699-708
Year:
2008-03
Contents:
The Fisher exact test has been unjustly dismissed by some as 'only conditional,' whereas it is unconditionally the uniform most powerful test among all unbiased tests, tests of size α and with power greater than its nominal level of significance α. The problem with this truly optimal test is that it requires randomization at the critical value(s) to be of size α. Obviously, in practice, one does not want to conclude that 'with probability x the we have a statistical significant result.' Usually, the hypothesis is rejected only if the test statistic's outcome is more extreme than the critical value, reducing the actual size considerably. The randomized unconditional Fisher exact is constructed (using Neyman-structure arguments) by deriving a conditional randomized test randomizing at critical values c(t) by probabilities γ(t), that both depend on the total number of successes T (the complete-sufficient statistic for the nuisance parameter—the common success probability) conditioned upon. In this paper, the Fisher exact is approximated by deriving nonrandomized conditional tests with critical region including the critical value only if γ (t) > γ0, for a fixed threshold value γ0, such that the size of the unconditional modified test is for all value of the nuisance parameter—the common success probability—smaller, but as close as possible to α. It will be seen that this greatly improves the size of the test as compared with the conservative nonrandomized Fisher exact test. Size, power, and p value comparison with the (virtual) randomized Fisher exact test, and the conservative nonrandomized Fisher exact, Pearson's chi-square test, with the more competitive mid-p value, the McDonald's modification, and Boschloo's modifications are performed under the assumption of two binomial samples.
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