What is a likelihood ratio in the context of DNA interpretation?

What is a likelihood ratio in the context of DNA interpretation?

• A. The ratio of the probability of the observed data under two competing hypotheses (e.g., contributor vs non-contributor).
• B. The ratio of the number of loci used to the total possible loci.
• C. The ratio of the DNA quantity to the sample size.
• D. The ratio of true positives to false positives.

Answer: (D)

In DNA interpretation, the likelihood ratio shows how much more (or less) likely the observed DNA evidence is under one competing explanation than under another. It’s a ratio of probabilities: LR = P(Evidence | Hypothesis that a specific person contributed) divided by P(Evidence | Competing hypothesis, such as another person contributed or no contributor). So, if the observed DNA profile is very common in the population, P(Evidence | the contributor) isn’t much higher than P(Evidence | the alternative), and the LR stays small. If the profile is rare and a specific person matches it well, P(Evidence | the contributor) becomes much larger than under the alternative, giving a large LR. Conversely, if the evidence fits poorly under the contributor hypothesis, the LR drops below 1, indicating support for the alternative. This isn’t about counts of loci, DNA quantity, or positives vs negatives. It’s about how strongly the data support one hypothesis over another. The larger the LR, the stronger the support for the contributor hypothesis; the smaller, the stronger the support for the alternative.

In DNA interpretation, the likelihood ratio shows how much more (or less) likely the observed DNA evidence is under one competing explanation than under another. It’s a ratio of probabilities: LR = P(Evidence | Hypothesis that a specific person contributed) divided by P(Evidence | Competing hypothesis, such as another person contributed or no contributor).

So, if the observed DNA profile is very common in the population, P(Evidence | the contributor) isn’t much higher than P(Evidence | the alternative), and the LR stays small. If the profile is rare and a specific person matches it well, P(Evidence | the contributor) becomes much larger than under the alternative, giving a large LR. Conversely, if the evidence fits poorly under the contributor hypothesis, the LR drops below 1, indicating support for the alternative.

This isn’t about counts of loci, DNA quantity, or positives vs negatives. It’s about how strongly the data support one hypothesis over another. The larger the LR, the stronger the support for the contributor hypothesis; the smaller, the stronger the support for the alternative.