Module Information

Module Identifier
Module Title
Topics in Biological Statistics
Academic Year
Semester 2
Other Staff

Course Delivery

Delivery Type Delivery length / details
Seminar 11 x 1 Hour Seminars
Practical 6 x 2 Hour Practicals
Lecture 8 x 2 Hour Lectures


Assessment Type Assessment length / details Proportion
Semester Assessment Report on one data set  1000-15000 words  30%
Semester Assessment 2 Workbook  2 x 3 weeks  70%
Supplementary Assessment Resubmited failed components  100%

Learning Outcomes

On successful completion of this module students should be able to:

Demonstrate understanding of advanced statistical topics.

Summarise and succinctly present results of statistical analysis.

Use the statistical programming language R to investigate topics in statistics.

Make effective choices about how to present statistical data.

Critically evaluate statistical data and make decisions about what inferenes can reasonably be drawn from particular data sets.

Present the context of a statistical data set, including how the data was collected, potential problems with analyzng the data and what message the data reveal.

Brief description

This course includes an introductory lecture in each of at least six advanced statistical topics. From this set of topics the students chooe two to study in more detail. The detailed study involves completing a workbook for each topic, for which the statistical programming language R is required.

The second part of the curse is on statistical consultancy. Students will be exposed to a range of different data sets. This section of the course is taught in a series of seminars, where group discussion and critical reflection are encouraged. Students are expected to assess the validity of the data collection methods, discuss possible approaches to analysis and think critically about what inferences may meaningfully be drawn from the available data. This includes discussions of published papers from various statistical fields.


The topics available (introductory lecture followed by computer-based workbook) include: principal component analysis, statistical epidemiology, multivariate ANOVA (MANOVA), time series, multifactorial ANOVA and generalized linear models (GLMs). The two-hour introductory lectures take place in the first three weeks of the semester. Each workbook is associated with three, two-hour computer practical sessions spread out over three weeks. The statistical programming language R is used for these workbooks. Each student will need to choose two of the workbooks to complete, and the marks for the workbooks will constitute the major part of the assessment for the module.

The statistical consultancy is presented as a set of weekly, one-hour seminars. Data sets are presented by postgraduate students and/or staff members, and through group discussions and individual reflection, students gain an understanding of how to approach new data sets and how to critically appraise different approaches to data analysis. Some of the seminars consist of discussions of peer-reviewed statistical articles, chosen to expose the group to contemporary issues in statistics and encourage critical thinking about statistical methodology.

Module Skills

Skills Type Skills details
Application of Number Necessary for completion of the two workbooks.
Communication Report writing on data set of student's choice.
Improving own Learning and Performance Time management relating to the completion of assignments and preparation for seminars.
Information Technology R statistical programming language used extensively in workbooks
Personal Development and Career planning Report writing - essential employability skill.
Problem solving Inherent in both aspects of the course (topics and statistical consultancy)
Research skills Research skills used for report writing.
Subject Specific Skills New statistical topics; learning to approach new data sets critically.
Team work Group discussions are a key part of the seminars for the statistical consultancy.


This module is at CQFW Level 6