The science of uncertainty
Undergraduate: Level 6
Thursday 08 October 2020
Friday 18 December 2020
04 June 2020
Requisites for this module
BA C841 Economics with Psychology,
BA C851 Economics with Psychology (Including Year Abroad),
BA C861 Economics with Psychology (Including Placement Year),
BSC C148 Economics with Psychology,
BSC C158 Economics with Psychology (Including Year Abroad),
BSC C168 Economics with Psychology (Including Placement Year)
The use of Bayesian statistics is increasingly common in psychology. This course aims to give a broad introduction to these tools, and how to use R (a popular, open source statistical software package) to analyse and visualise data. It will also give you an overview of how the human brain deals with uncertainty and probabilities, and how the media often misrepresents statistical issues. Throughout the course, you will gain familiarity with working with large datasets, 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.
This module will provide students with an opportunity to develop advanced research skills. Students who take this module will learn how Bayesian methods allow us to characterise uncertainty, both in empirical data and our beliefs and perception about the world. They will also gain experience in thinking critically about statistics, and learn about how the human brain deals with probabilities and uncertainty.
At the end of the module, students should:
1. be able to do basic data processing in R
2. create appropriate and engaging data visualisations
3. understand the differences between Bayesian and frequentist approaches to data analysis
4. be able to fit Bayesian linear models to data, visualise the results, and make new predictions
5. give examples of when our brains can handle uncertainty, and when they can’t
Learning outcome 1, 2, and 4 will be assessed by regular homework assignments throughout the term, and an open-book computer test at the end of term. Outcomes 3 and 5 will be assessed in an exam.
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. The course textbook is Statistical Rethinking (Mc Elreath, 2016).
This module is primarily taught as a reading course, and students are expected to read a chapter each week from the course textbook. Voluntary support sessions (online and/or lab based) will be provided each week and students are encouraged to attend and ask any questions they may. In addition, there are four compulsory lectures throughout the term.
- McElreath, Richard. (2016) Statistical rethinking: a Bayesian course with examples in R and Stan, Boca Raton: CRC Press/Taylor & Francis Group.
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.
Module supervisor and teaching staff
Dr Alasdair Clarke, email: firstname.lastname@example.org.
Dr Alasdair Clarke
No external examiner information available for this module.
Available via Moodle
Of 19 hours, 3 (15.8%) hours available to students:
16 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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