Hypothesis testing with the similarity index

Multilocus DNA fingerprinting methods have been used extensively to address genetic issues in wildlife populations. Hypotheses concerning population subdivision and differing levels of diversity can be addressed through the use of the similarity index ( S ), a bandsharing coefficient, and many researchers construct hypothesis tests with S based on the work of Lynch. It is shown in the present study, through mathematical analysis and through simulations, that estimates of the variance of a mean S based on Lynch's work are downwardly biased. An unbiased alternative is presented and mathematically justified. It is shown further, however, that even when the bias in Lynch's estimator is corrected, the estimator is highly imprecise compared with estimates based on an alternative approach such as 'parametric bootstrapping' of allele frequencies. Also discussed are permutation tests and their construction given the interdependence of S s which share individuals. A simulation illustrates how some published misuses of these tests can lead to incorrect conclusions in hypothesis testing.