Overview

This course focuses on the principles of Bayesian analysis with the aim to enable participants to apply Bayesian methods on their own research and understand other people's results via Bayesian analysis.

Content

At the end of the course, delegates should be able to think statistically using Bayesian reasoning and have the tools to implement such reasoning formally into a statistical analysis of data. In particular, delegates will be able to:

• Differentiate the principles of both classical and Bayesian statistical frameworks, and the advantages / disadvantages of each.
• Understand the concept of a probability distribution, including the main established distributions (e.g. the normal distribution) and their parameters.
• Recite the different contributing probability distributions that play a role in Bayes theorem of conditional probabilities.
• Incorporate their prior beliefs into an analysis alongside evidence presented by the collected data.
• Use the results of Bayesian analysis to answer their research question (through prediction, estimation hypothesis testing and credible intervals).
• Have appreciation for the computational complexity of fitting some Bayesian models

Course structure and teaching

This is an online, self-paced course that includes:

• Full electronic notes
• Short lecture videos that follow closely with the notes.
• Accessible materials with alternative text for images, and captions/transcripts for each video.
• Interactive quizzes for each chapter.
• Revision summaries.
• Support will also be available through a forum, where you can ask questions related to the course materials.

Learning outcomes

At the end of the course, delegates should be able to conduct a basic statistical analysis in R using their own data or data supplied in the course materials. Delegates should have reasonable confidence in further independent progress through access to help files. More specifically, delegates should:

• Be familiar with the Rstudio interface and menu options.
• Have the ability to use R as a basic calculator
• Understand the general structure of the language including objects, the use of brackets and functions.
• Be able to subset and manipulate the data into a format ready for statistical analysis
• Run functions to create basic summary statistics of the data, produce graphs of a publishable standard and perform statistical significance tests.
• Have confidence in learning and installing additional packages.

Entry requirements

A basic level of statistical literacy is required as a prerequisite.

It is desirable for the course participants to have basic knowledge of statistics, i.e. notion of statistical inference, p-values and Confidence intervals.

Those who have completed the five-day Introduction to Statistics and Research Methods course run frequently by the Centre for Applied Statistics Courses (CASC) team will be equipped.

Cost and concessions

The standard price is £75.

A 50% discount is available for UCL staff, students, alumni. If you're eligible for a discount, email ich.statscou@ucl.ac.uk before booking to be sent the discount code.

The course is available for free to those associated with the Institute of Child Health or Great Ormond Street Hospital, and SLMS doctoral students. Please also email ich.statscou@ucl.ac.uk to receive a booking code.

Certificates

You can download a certificate of participation once you have completed all the session quizzes.

Find out about other statistics courses

CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses allow non-statisticians to interpret published research and/or undertake their own research studies.

Find out more about CASC's full range of statistics courses.

Course team

Dr Dean Langan

Dean works as a lecturer, jointly based within the School of Life and Medical Sciences (SLMS) and the Centre for Applied Statistics Courses (CASC) at University College London (UCL). He is responsible for the development and implementation of online statistical training principally aimed at doctoral students within SLMS. He took up this position in August 2021 having worked for 6 years solely within CASC, delivering face-to-face short courses to a wide range of delegates, including students and staff within UCL Great Ormond Street Institute of Child Health, and other working professionals from a wide range of industries.

Previously, Dean has also worked as a medical statistician at the Clinical Trials Research unit (CTRU, University of Leeds), working on a number of phase II and III clinical trials in a range of disease areas including stroke and myeloma cancer. He undertook a PhD full-time at the University of York from 2012 to 2015, researching statistical methods for meta-analysis. The title of the PhD was 'estimating the heterogeneity variance in a random-effects meta-analysis'.

Course information last modified: 11 May 2023, 17:54