The statistical analysis of mitochondrial DNA polymorphisms: chi 2 and the problem of small samples.
Significance levels obtained from a chi 2 contingency test are suspect when sample sizes are small. Traditionally this has meant that data must be combined. However, such an approach may obscure heterogeneity and hence potentially reduce the power of the statistical test. In this paper, we present a Monte Carlo solution to this problem: by this method, no lumping of data is required, and the accuracy of the estimate of alpha (i.e., a type 1 error) depends only on the number of randomizations of the original data set. We illustrate this technique with data from mtDNA studies, where numerous genotypes are often observed and sample sizes are relatively small.
Roff
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Department of Biology, McGill University, Montreal, Quebec, Canada.
Bentzen
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D000465:Algorithms; D000818:Animals; D049872:Bivalvia / Q000235:genetics; D004272:DNA, Mitochondrial / Q000235:genetics*; D005399:Fishes / Q000235:genetics; D005821:Genetic Techniques; D011110:Polymorphism, Genetic; D012494:Sampling Studies; D013223:Statistics as Topic / Q000379:methods*
D004272:DNA, Mitochondrial