The saving point is that the features in which we are interested are believed theoretically and found empirically to be essentially uncorrelated with the means by which samples are chosen. It is difficult, expensive, and impractical to arrange a statistically valid random-sampling scheme. A second is that we are forced to rely on convenience samples, chosen not at random but because of availability or cost. One is a lack of agreement as to what the relevant population is (should it be the whole population or only young males? should it be local or national?) and the consequent need to consider several possibilities. Several conditions make the actual situation less than ideal. Ideally, the reference data set from which genotype frequencies are calculated would be a simple random sample or a stratified or otherwise scientifically structured random sample from the relevant population. Our approach is empirical: we compare different subpopulations and also, to mimic a worst case scenario, perform sample calculations deliberately using an inappropriate database.Ī simple random sample of a given size from a population is one chosen so that each possible sample has an equal chance of being selected. Although the formulae might provide good estimates of the match probability for the average member of the population, they might not be appropriate for a member of an unusual subgroup. The second issue, the subpopulation problem, is broader than the first. If the database is small, the values derived from it can be uncertain even if it is compiled from a scientifically drawn sample this can be addressed by providing confidence intervals on the estimates. In the first instance, inferences based on values in a database might be uncertain because the database is not compiled from a sample of the most relevant population or the sample is not representative. The other might be called the subpopulation problem. One is associated with the characteristics of a database, such as its size and whether it is representative of the appropriate population. Two major issues regarding uncertainty must be addressed in the statistical evaluation of DNA evidence. The chapter includes a discussion of the statistics of matching and binning of VNTRs. Finally, we ask how many loci would be needed to establish a profile as unique.
We also discuss the degree of uncertainty of such estimates according to statistical theory and empirical tests that use different databases.
#STATISTICAL CALCULATIONS OF MIXED DNA SAMPLES HOW TO#
In this chapter, we consider how to interpret frequencies as probabilities and likelihood ratios and how to make adjustments when a suspect is found through a database search. In Chapter 4, we presented ways to estimate the frequencies of genotypes and profiles in the population.
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