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Case Study
STATISTICAL CRIME FIGHTERS:
IMPROVING FORENSIC SCIENCE
Scientific evidence has a crucial role to play in the
administration of justice. It is important that it is evaluated
objectively and interpreted clearly for the courts, since failure
to do so can lead to appeals or even miscarriages of justice.
THE COMPARISON
PROBLEM
Professor Colin Aitken and his collaborators have developed
Bayesian statistical methods to aid in the evaluation and
interpretation of forensic scientific evidence. These new
methods have reduced cost, increased accuracy and
improved the interpretation of the value of evidence.
ASSESSING UNCERTAINTY
An understanding of the value of forensic evidence relies
heavily on an assessment of uncertainty. Imagine, for
example, that fragments of glass found at a crime scene are
believed to come from a broken bottle found in possession
of a suspect. The chemical composition of the glass in the
fragments and in the bottle is analysed. What is the value of
similarities between the composition of the two samples? In
order to improve the evaluation of such evidence, Professor
Aitken and other researchers from the Maxwell Institute for
Mathematical Sciences developed new Bayesian statistical
methods. These methods enable forensic scientists worldwide
to interpret their data reliably.
The glass fragments can come from the bottle found in
possession of the suspect, or they may come from another
bottle. To help the court estimate the relative likelihood of
these two possibilities, Professor Aitken and collaborators
calculate a so-called likelihood ratio (LR) that takes into
account variations within glass bottles and between multiple
bottles, first assuming the fragments came from the suspect’s
bottle, and second assuming they came from another bottle.
TRANSFORMATIVE IMPACT
A senior forensic statistician at the Netherlands Forensic
Institute says: ‘The groundbreaking work of Aitken and others
has transformed the way we evaluate forensic evidence … the
LR method is the next step in the evolution from forensic craft
to forensic science.’
In comparison of evidence involving glass,
a key question is how much support
measurements give to the proposition that
the fragments in the upper picture
originated from the bottle in the lower
picture as compared with the proposition
that they did not.
In another scenario, when large consignments of potentially
incriminating material are seized (such as pills suspected
of containing illegal drugs, or computer files suspected of
containing illegal material), the police want to estimate the
proportion that is illicit.
PROBABILITIES
IN THE BALANCE
Examination of every item is time-consuming, costly and
stressful. Professor Aitken developed procedures for
determining the optimal size of samples that should be
examined. Through a careful assessment of the uncertainties
associated with an examination of only a fraction of a
consignment, his research ensures that investigators can
sample fewer items and still provide evidence that is fit for
purpose in a criminal trial.
Professor Aitken’s sampling protocols have been widely
adopted. They have been recommended to forensic
laboratories by the Crown Office in Scotland and in guidelines
by the United Nations Office on Drugs and Crime.
Sampling software based on Professor Aitken’s statistical
methods is available through the European Network of
Forensic Science Institutes. This software allows forensic
scientists without a strong background in statistics to benefit
from cutting-edge Bayesian statistical methods.
RELEVANT RESEARCH PUBLICATIONS
FROM THE AITKEN GROUP
Evaluation of evidence: the left-hand
scale shows the weight associated with
the prosecution’s proposition Hp (that the
suspect is guilty) as the logarithm of the
probability of the evidence E if Hp true.
The right-hand scale shows the weight
associated with the defence proposition
Hd (that the suspect is innocent) as the
logarithm of the probability of the evidence
E if Hd true.
Aitken, CGG (1999) ‘Sampling – how big a sample?’ Journal of
Forensic Sciences, 44, pp 750-760.
Aitken, CGG and Lucy, D (2002) ‘Estimation of the quantity
of a drug in a consignment from measurements on a sample’
Journal of Forensic Sciences, 47, pp 968-975.
Aitken, CGG and Lucy, D (2004) ‘Evaluation of trace evidence
in the form of multivariate data’ Applied Statistics, 53,
pp 109-122, with corrigendum pp 665-666.
Aitken, CGG, Zadora, G and Lucy, D (2007) ‘A two-level model
for evidence evaluation’ Journal of Forensic Sciences, 52,
pp 412-419.
Aitken, CGG. and Taroni, F (2004, 2nd edition), ‘Statistics and
the evaluation of evidence for forensic scientists’
John Wiley & Sons Ltd.
CONTACT
Professor Colin GG Aitken
Maxwell Institute for Mathematical Sciences
School of Mathematics
+44 (0)131 650 5060
[email protected]
www.maths.ed.ac.uk/~cgga
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