The science of uncertainty
Undergraduate: Level 6
Thursday 05 October 2023
Friday 15 December 2023
13 November 2023
Requisites for this module
The use of Bayesian statistics is increasingly common in psychology and other fields of science. This module aims to introduce you to this approach and to how to apply it in practice using R (a popular, open source statistical software package).
This module will also give you an overview of the foundations of statistical science and probability theory, of how we deal with uncertainty and probabilities in everyday decisions, and of how the media often misrepresents statistical issues. Throughout the module, you will gain familiarity with analysing data in R, identifying patterns and presenting data. These skills are useful not only for further postgraduate study, but also are increasingly valuable in graduate jobs outside academia.
The aims of this module are:
- To introduce students to the use of Bayesian analysis and to how to apply it in practice using R (a popular, open source statistical software package).
- To give students an overview of the foundations of statistical science and probability theory, of how we deal with uncertainty and probabilities in everyday decisions.
By the end of this module, students will be expected to be able to:
- Do basic data processing in R.
- Create appropriate and engaging data visualisations.
- Understand the differences between Bayesian and frequentist approaches to data analysis.
- Fit Bayesian linear models to data, visualise the results, and make new predictions.
- Give examples of when our brains can handle uncertainty, and when they can’t.
- Critically evaluate conclusions drawn from data in the news, media and research.
Learning outcomes 1, 2, and 4 will be assessed by regular homework assignments throughout the term, and in the openbook computer based test. Outcomes 3 and 5 will also be assessed in the open-book test.
Lectures start on the hour. Please arrive promptly to avoid disrupting the class. Please ask questions during class if there is anything that is unclear. The drop-in support sessions are optional, but you are encouraged to attend. This course requires using the programming language R.
This module will be delivered via:
- Lab-based support sessions.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list
Assessment items, weightings and deadlines
|Coursework / exam
||Class Test - Open book
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Module supervisor and teaching staff
Dr Alasdair Clarke, email: firstname.lastname@example.org.
Dr Alasdair Clarke
Dr Paula Miles
University of St Andrews
Director of Teaching, Senior Lecturer
Available via Moodle
Of 22 hours, 20 (90.9%) hours available to students:
2 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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