Extraction of the relevant population from a forensic database

Abstract

Evaluation of forensic evidence using Bayesian statistics requires the formulation of hypotheses. Many hypotheses, especially those presenting the defence viewpoint imply that traces can be attributed to an arbitrary member of a relevant population. The exact items or persons that comprise the relevant population may vary from case to case. Therefore, the statistical evaluation of evidential value based on databases cannot make use of a fixed set of items or persons. In the current paper, methodology is presented to filter the contents of a database such that only items that are considered relevant are selected. Six scenarios, including those related to fibre, textile, and glass evidence are described, together with the hypotheses and relevant populations that may be evaluated by an expert. In addition, we show how items representing the defined relevant population can be extracted from a database using SQL code. Images of the items in the (filtered) relevant population provide an overview of the selected items and hence direct feedback to the examiner. In this way, erroneous codes or unwanted side effects can be identified and corrected. It is concluded that the filtering procedure is effective in cases where the relevant population is demarcated accurately.