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Course Sheet Academic Year of enrolment:
Professor and Collaborators:
Hours of classroom activity:
Prerequisites:
Basic knowledge of calculus
Objectives
Contents The following topics are considered important parts of the teaching program for the fulfilment of the objectives: probability theory and Bayes Theorem in forensic statistics; forensic evidence evaluation
Extended Syllabus 1. Conditional probability and Bayes Theorem
- Empirical probabilities
2. Evidence evaluation
- Forensic Evidences types
- The value of evidence
- Significance testing and evidence evaluation
- Relevance and the formulation of propositions
3. Evaluation of evidence in practice and example
- Which database to use
- Type and geographic factors
- DNA and database selection
- Verbal equivalence of the likelihood ratio
- Some common criticisms of statistical approaches
- Blood group frequencies
- Trouser fibres
- Shoe types
- Airweapon projectiles
- Height description from eyewitness
- DNA
4. Errors in interpretation
Statistically based errors of interpretation (Transposed conditional;
Defender’s fallacy; Numerical conversion error)
Methodological errors of interpretation
Different level error
Defendant’s database fallacy
Independence assumption
Recommended Bibliography Materials (e.g. slides) can be downloaded from https://fad.unich.it/ .
Suggested Textbooks
David Lucy, Introduction to Statistics for Forensic Scientist, Wiley, 2005
James Michael Curran, Introduction to Data Analysis with R for Forensic Scientists, CRC Press 2010
Teaching Methods Frontal lectures as well as practical exercises with the use of CALC. Attendance to teaching activities, even if not compulsory, is strongly recommended
Evaluation methods Verification of learning:
The exam consists of carrying out a project and discussing the project in an oral examination aimed at assessing the knowledge related to the topics covered in the course.
Contacts/More Information