• Home
• Study
• Study Information
• Modules
• Module Information

Analysis of Biological Data

Module description

An understanding of the analysis of biological data is an essential tool in biological research. During this module you will become familiar with the World’s most powerful, flexible and free (!) statistical analysis software. You will work through example problems during lectures and practical sessions, learn how to choose and apply a variety of analytical approaches and learn how to design experiments to maximise the chance of making discoveries.

Module aims - intentions of the module

The aim of this module is to provide training in the collection and analysis of biological datasets, recognising that statistics is a tool for understanding data and not an end in itself. The module is based around understanding the logic behind statistical tests and applying them using the open-access statistics program ‘R’. We use an interactive approach working through examples of analyses using R. The module aims to provide you with early training for the outputs expected from your research projects, particularly in the final year.

You will gain knowledge in and experience of the modern scientific method as applied in ecology and evolution, including hypothesis formulation, experimental design and modern techniques for collecting and analysing data. We use real research data that you collect in our practicals, and discuss examples from contemporary biological research in lectures. Data analysis is becoming an increasingly important aspect of the running of modern businesses of all types. An understanding of the framework within which we can ask questions using data is an important skill for a growing range of careers, and competence with R is an increasingly valuable asset.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. Discuss the modern scientific process, demonstrating an appreciation of the inherent constraints on the generation of objective knowledge via subjective experience
• 2. Formulate testable hypotheses about ecological processes given practical limitations on data collection
• 3. Recognise, contrast and apply methods of data collection and analysis, including non-parametric and parametric methods, recognising the assumptions and limitations of these approaches
• 4. Use the statistics program ‘R’ to conduct a range of analyses, and to produce simple plots in order to visualise data prior to more quantitative analyses

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 5. Describe in some detail essential facts and theory across a sub-discipline of biosciences
• 6. Identify critical questions from the literature and synthesise research-informed examples from the literature into written work
• 7. With some guidance, deploy established techniques of analysis, practical investigation, and enquiry within biosciences
• 8. Describe and evaluate approaches to our understanding of biosciences with reference to primary literature, reviews and research articles

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 9. Develop, with some guidance, a logical and reasoned argument with valid conclusions
• 10. Communicate ideas, principles and theories fluently using a variety of formats in a manner appropriate to the intended audience
• 11. Collect and interpret appropriate data and complete research-like tasks, drawing on a range of sources, with limited guidance
• 12. Evaluate own strengths and weaknesses in relation to professional and practical skills, and apply own evaluation criteria
• 13. Reflect effectively on learning experiences and summarise personal achievements

Syllabus plan

This module will be delivered via a hybrid approach, with recorded lectures and face to face discussions and practical sessions.

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics: 

• The philosophy of science and experimental design
• Types of data and summary statistics
• Entering and analysing data in R
• Testing assumptions and hypotheses in R

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to sit a further examination. The mark given for a re-assessment taken as a result of referral will count for 100% of the final mark and will be capped at 40%.

Indicative learning resources - Basic reading

• Whitlock, M. 2008. The Analysis of Biological Data. Roberts and Company Publishers
• Crawley, M.J. 2005. Statistics: An introduction using R. Wiley

Indicative learning resources - Web based and electronic resources

• ELE page: http://vle.exeter.ac.uk/course/view.php?id=330 (includes 7 sets of downloadable help sheets designed for this module)

Key words search

Statistics, Data Collection, ANOVA, Correlation, Regression, Probability, Data Distribution, Variance, R Programming language, Graphics and Experimental Design.