35255 Forensic Statistics
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Credit points: 6 cp
Result type: Grade and marks
There are course requisites for this subject. See access conditions.
Description
This subject provides students with an understanding of the role of probabilistic inference in forensic science and interpretation of the fundamental unit of forensic science, 'the trace'. Students gain skills in using the logical approach to interpret observations and results in the context of practical forensic examples, case studies, and databases of forensic data. This subject aims to provide students with foundational knowledge in forensically relevant probability theory, statistical analysis and modelling methods to prepare them for subsequent subjects and apply to their specific area of expertise.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Understand the role of propositions and inference in forensic science |
|---|---|
| 2. | Apply probability theory to the interpretation of traces |
| 3. | Evaluate the value of traces in a wide range of scenarios encountered in forensic science (including source level and activity level questions, database hits, and multiple traces) |
| 4. | Explain the value of the evidence in verbal and written forms |
| 5. | Analyse and visualise data using the R coding language |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Demonstrate a command of forensic science practice, including the detection, collection, and analysis of traces in order to exploit and integrate the results of analyses into investigative, evaluative and intelligence frameworks. (1.1)
- Apply investigative, critical thinking and problem-solving skills to forensic science problems and design experimental methods to test hypotheses and critically analyse and interpret data. (2.1)
- Demonstrate skills in communicating experimental conclusions, expert opinion, and the justification of professional decisions related to forensic science processes effectively to expert, scientific, and non-expert audiences. (5.1)
Contribution to the development of graduate attributes
Forensic Statistics is a foundational subject within the forensic science degree program at UTS. Skills learnt in this subject will be applied throughout the remainder of the course and are key to graduate success. Students are introduced to the problem-solving, scientific and communication skills required in a professional context. Upon completion of this subject, students should be able to assign appropriate evidential weight to various types of traces in the context of cases typically encountered in forensic science.
Graduate Attribute 1 – Disciplinary Knowledge
Students will develop a working knowledge of forensic science practice, evaluation of traces and integration of the derived information with the investigative and legal systems in the lectures and workshops. Students will learn how to formulate an appropriate pair of propositions based on the case circumstances, assess the probability of the findings given a proposition for multiple types of traces, and present the weight of evidence in a logical and transparent manner. This will be assessed in assessment tasks 1 and 3.
Graduate Attribute 2 – Research, Inquiry and Critical Thinking
Students will investigate case examples in the tutorial activities and engage in group activities to solve trace evaluation problems using knowledge drawn from the lectures, computer labs and from self-directed learning activities. This will be assessed in all three assessment tasks.
Graduate Attribute 5 – Communication
Students will be introduced to the skills required for professional-level communication in forensic science. Students will learn and be assessed on their oral and written communication skills in the presentation of probabilistic information. Students will be able to develop their oral communication skills during the computer labs where there will be opportunities for them to present their findings where they can receive informal feedback. Students written communication skills will be assessed in assessment tasks 2 and 3.
Teaching and learning strategies
This subject will be delivered through lectures, computer labs, workshops and independent learning activities.
Lectures
There will be 2 hours of lectures each week. These sessions introduce and explain key principles in forensic statistics and relate them to modern professional practice. The lectures also offer opportunities for questions and clarification of subject material. Attendance is recommended at all lectures to develop a complete understanding of the content. Students should read the relevant reading material provided on Canvas, as this will prepare the students for active discussion in class. All resources used in the lectures will be available through Canvas before the scheduled classes, allowing students to concentrate on and contribute to the discussion in class. Case studies are integrated extensively throughout the lectures to provide a context for the theory being presented.
Computer Labs
The computer labs are an essential part of the subject as they will consolidate a student's understanding of concepts delivered in the lectures. Students will work in groups to solve problems and will be required to present their results verbally to the class and in written form. These will be followed by class discussions and oral feedback. More specifically, computer lab activities will include learning and exploring the use of RStudio for solving problems, an introduction to data analysis, and producing graphs to visually represent specific aspects of a dataset. Attendance for the computer lab program is compulsory and assessed through two graded assessment tasks conducted in groups (see Assessment Tasks 2 and 3). Written/Oral feedback will be given to each group for each of these assessment tasks.
Workshops
The workshops are an opportunity for students to engage in in-depth discussions of topics presented in lectures, obtain additional explanations on these topics, and explore the interpretation of evidence in mock case scenarios. More specifically, workshop activities will include studying case scenarios, assessing expert witness statements, and evaluating forensic results for various types of traces in mock case scenarios. It is mandatory to complete the previous week’s assessment task in order to attend the workshops each week.
Independent Learning Activities
Independent learning activities with structured feedback are employed in Forensic Statistics, accessed through Canvas. These take the form of self-directed online activities. These online activities accompany the lectures throughout the semester to provide students with learning opportunities and individual feedback prior to the larger assessment activities. The online activities contain compulsory exercises that are formally assessed, and contribute to the student’s final grade. After the submission deadline, the student will be able to see which exercises they got right or wrong, and this will give them individual feedback on how well they understood each topic. The weekly labs of the week before will be discussed during the workshops in the next week and no submissions for the previous week's lab will be accepted once the solutions are shared during the workshops.
Written Communication Task
An aim of this subject is to help you develop academic and professional language and communication skills to succeed at university and in the workplace. During the course of this subject, you will complete a milestone assessment task that will, in addition to assessing your subject-specific learning objectives, assess your English language proficiency.
Content (topics)
- Inference in forensic science. Introduction to probability theory, laws of probability, conditional probability, Bayes’ theorem (probabilities), reasoning processes.
- Weight of evidence. Match probability, likelihood ratio, Bayes’ theorem (odds), working with Bayes’ theorem, reporting a likelihood ratio. Transposed conditional, prosecutor’s fallacy, defence attorney’s fallacy, uniqueness fallacy.
- Hierarchy of propositions. Source, activity and offense level propositions and evidence assessment.
- Evidence assessment at the activity level. Likelihood ratios, transfer probabilities, background probabilities. Evaluation of glass evidence. Evaluation of fibre evidence.
- Evidence assessment at the offense level. Likelihood ratios, relevance of a trace, relationship between offense and source level propositions, combining evidence.
- Formulating propositions. Propositions and explanations, defining the relevant population.
- Evidence assessment for combining evidence. Likelihood ratio, two-trace problem, database search problem.
- Introduction to forensic genetics. Concepts of population genetics, Hardy-Weinberg equilibrium, linkage equilibrium, population genetic models. Validation of population genetic models, independence tests, Fisher's exact test, Bonferonni correction.
- Validation of forensic methods. Discrimination, misleading evidence, performance, calibration, Tippett plots.
- Introduction to Bayesian networks (BNs). Definition of BNs, propagation of uncertainty in a BN, fundamental types of connections. Fundamental structures for evidence assessment at source, activity and offense levels.
Assessment
Assessment task 1: Independent Learning Activities
| Intent: | This assessment task contributes to the following graduate attributes: 1. Disciplinary Knowledge 2. Research, Inquiry and Critical Thinking |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1 and 2.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 40% |
| Criteria: |
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Assessment task 2: RStudio Data Analysis
| Intent: | This assessment task contributes to the following graduate attributes: 2. Research, Inquiry and Critical Thinking 5. Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 2.1 and 5.1 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 25% |
| Criteria: |
|
Assessment task 3: Evaluation of Evidence Report
| Intent: | This assessment task contributes to the following graduate attributes: 1. Disciplinary Knowledge 2. Research, Inquiry and Critical Thinking 5. Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 5.1 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 35% |
| Criteria: | Students will be assessed based on:
|
Minimum requirements
A student should demonstrate competence in all aspects of the assessment in order to pass the subject. To pass the subject, a student must achieve a final result of 50% or more. The final result is simply the sum of the marks gained in each piece of assessment.
Required texts
Each week has a mandatory reading assignment. These assignments include relevant excerpts from the recommended texts and other resources listed in the sections below. PDF files of the required texts will be made available each week on Canvas under "Reading List".
Recommended texts
B. Robertson, G.A. Vignaux, C.E.H. Berger. Interpreting Evidence. John Wiley & Sons, Chichester, 2nd edition, 2016, ISBN 9781118492482.
C.G.G. Aitken, P. Roberts, G. Jackson. Practitioner Guide No. 1: Fundamentals of Probability and Statistical Evidence in Criminal Proceedings. Royal Statistical Society, 2010. (full text available on Canvas under "Reading List")
C.G.G. Aitken and F. Taroni. Statistics and the Evaluation of Evidence for Forensic Scientists. John Wiley & Sons, Chichester, 2nd edition, 2004, ISBN 0-470-84367-5.
D.V. Lindley. Understanding Uncertainty. John Wiley & Sons, Chichester, 2nd edition, 2013, ISBN 9781118650127.
Other resources
J.S. Buckleton, J.-A. Bright, D. Taylor. Forensic DNA Evidence Interpretation. CRC Press, Boca Raton, 2nd edition, 2016, ISBN 9781482258899.
J.M. Curran. Introduction to Data Analysis with R for Forensic Scientists. CRC Press, Boca Raton, 2011, ISBN 978-1-4200-8826-7.
P. Roberts, C.G.G. Aitken. Practitioner Guide No. 3: The Logic of Forensic Proof: Inferential Reasoning in Criminal Evidence and Forensic Science. Royal Statistical Society, 2014.
F. Taroni, C.G.G. Aitken, P. Garbolino, A. Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. John Wiley & Sons, Chichester, 2006, ISBN 9780470091739.
F. Taroni, A. Biedermann, S. Bozza, P. Garbolino, C.G.G. Aitken. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. John Wiley & Sons, Chichester, 2nd edition, 2014, ISBN 9780470979730.
F. Taroni, S. Bozza, A. Biedermann, P. Garbolino, C.G.G. Aitken. Data Analysis in Forensic Science: A Bayesian Decision Perspective. John Wiley & Sons, Chichester, 2010, ISBN 9780470998359.