What should a forensic practitioner's likelihood ratio be?

A practical treatment of sensitivity analyses in activity level evaluations

Evaluations of forensic observations considering activity level propositions are becoming more common place in forensic institutions. A measure that can be taken to interrogate the evaluation for robustness is called sensitivity analysis. A sensitivity analysis explores the sensitivity of the evaluation to the data used when assigning probabilities, or to the level of uncertainty surrounding a probability assignment, or to the choice of various assumptions within the model. There have been a number of publications that describe sensitivity analysis in technical terms, and demonstrate their use, but limited literature on how that theory can be applied in practice. In this work we provide some simplified examples of how sensitivity analyses can be carried out, when they are likely to show that the evaluation is sensitive to underlying data, knowledge or assumptions, how to interpret the results of sensitivity analysis, and how the outcome can be reported. We also provide access to an application to conduct sensitivity analysis.

Forensic botany: who?, how?, where?, when?

Plants are a good source of biological forensic evidence; this is due to their ubiquity, their ability to collect reference material, and their sensitivity to environmental changes. However, in many countries, botanical evidence is recognised as being scientifically. Botanical evidence is not mostly used for perpertration, instead it tends to serve as circumstantial evidence. Plant materials constitute the basis, among others, for linking a suspect or object to a crime scene or a victim, confirming or not confirming an alibi, determining the post-mortem interval, and determining the origin of food/object. Forensic botany entails field work, knowledge of plants, understanding ecosystem processes, and a basis understaning of geoscience. In this study, experiments with mammal cadavers were conducted to determine the occurence of an event. The simplest criterion characterising botanical evidence is its size. Therefore, macroremains include whole plants or their larger fragments (e.g. tree bark, leaves, seeds, prickles, and thorns), whereas microscopic evidence includes palynomorphs (spores and pollen grains), diatoms, and tissues. Botanical methods allow for an analysis to be repeated multiple times and the test material is easy to collect in the field. Forensic botany can be supplemented with molecular analyses, which, although specific and sensitive, still require validation.

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™

Probabilistic genotyping has become widespread. EuroForMix and DNAStatistX are both based upon maximum likelihood estimation using a γ model, whereas STRmix™ is a Bayesian approach that specifies prior distributions on the unknown model parameters. A general overview is provided of the historical development of probabilistic genotyping. Some general principles of interpretation are described, including: the application to investigative vs. evaluative reporting; detection of contamination events; inter and intra laboratory studies; numbers of contributors; proposition setting and validation of software and its performance. This is followed by details of the evolution, utility, practice and adoption of the software discussed.

Bayesian multivariate models for case assessment in dynamic signature cases

Dynamic signatures are recordings of signatures made on digitizing devices such as tablet PCs. These handwritten signatures contain both dynamic and spatial information on every data point collected during the signature movement and can therefore be described in the form of multivariate data. The management of dynamic signatures represents a challenge for the forensic science community through its novelty and the volume of data available. Much as for static signatures, the authenticity of dynamic signatures may be doubted, which leads to a forensic examination of the unknown source signature. The Bayes' factor, as measure of evidential support, can be assigned with statistical models to discriminate between competing propositions. In this respect, the limitations of existing probabilistic solutions to deal with dynamic signature evidence is pointed out and explained in detail. In particular, the necessity to remove the independence assumption between questioned and reference material is emphasized.

How can courts take into account the uncertainty in a likelihood ratio?

As legal practitioners and courts become more aware of scientific methods and evidence evaluation, they are demanding measures of the reliability of expert opinion. In particular, there are calls for error rates to accompany opinion evidence in comparative forensic sciences. While error rates or confidence intervals can be useful for those disciplines that claim to identify the source of a trace, the call for these statistical tools has extended to sciences that present opinions in the form of a likelihood ratio. In this article we argue against presenting both a likelihood ratio and numerical measures of its uncertainty. We explain how the LR already encapsulates uncertainty. Instead we consider how sensitivity analyses can be used to guide the presentation of LRs that are informative to the court and not unfair to defendants.