Catalogue of Modules, University of Nottingham

C14703 Advanced Experimental Design and Analysis
(Last Updated:03 May 2017)

Year  11/12

Total Credits: 10

Level: Level 4

Target Students:  Students registers for MSci degrees within the School of Biology.

Taught Semesters:

Autumn Assessed by end of Autumn Semester 

Prerequisites: Registered for a degree in the School of Biology. Only available to Year 4 students.

Corequisites:  None.

Summary of Content:  An advanced level biological statistics course, building on basic undergraduate (Levels 1 and 2) training. Lectures discuss concepts in experimental design, biological probability, generalised linear modelling and multivariate statistics. Practical sessions build on this conceptual outline, giving hands on experience of problem solving and analytical software, and some basic programming skills.

Method and Frequency of Class:

ActivityNumber Of WeeksNumber of sessionsDuration of a session
Lecture 11 weeks1 per week2 hours
Workshop 4 weeks1 per week1 hour

Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

Method of Assessment: 

Assessment TypeWeightRequirements
Coursework 1 25 Answers to workshop problems 
Coursework 2 25 Answers to workshop problems 
Coursework 4 25 Answers to workshop problems 
Inclass Exam 1 (Written) 25 Written exam - 1 hour 

Dr T Reader

Education Aims:  Building on basic undergraduate training, this module will outline a range of statistical techniques that students are likely to encounter during their research projects. We will also discuss the most common experimental design problems faced by biologists. The objective is not to give detailed training in all techniques, but to provide a conceptual toolkit enabling students to develop solutions to a range of problems, and a basis from which to explore the relevant literature and software in their own time.

Learning Outcomes:  Students will: A2. Learn about current trends and developments in approaches to the design of experiments and the analysis of complex datasets in biology. A4. Learn to use appropriate terminology in statistics and experimental design when talking about their work. B1. Critically analyse and interpret published information and data. B3. Understand classical and complex problems in experimental designs and learn to recognise them in real biological scenarios. C1. Tackle research questions using quantitative analysis of data. C4. Undertake appropriate experimental design and statistical analysis. D4. Use and access information technology, including statistical packages and a programming language. D6. Manage and manipulate numerical data.

Offering School:  Life Sciences

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