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Analysis of Biological Data

Module titleAnalysis of Biological Data
Module codeBIO2426
Academic year2022/3
Module staff

Dr Kelly Moyes (Convenor)

Duration: Term123
Duration: Weeks


Number students taking module (anticipated)


Description - summary of the module content

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

Syllabus plan

Lectures will include topics such as:

  • 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 and teaching

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

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching15Lectures – including material covering the language used in R, data import, manipulating data, ANOVA, correlation, regression and graphics
Scheduled learning and teaching12Computer practical sessions – training in the use of R programming language and statistical tests (4 x 3 hours)
Guided independent study123Additional reading, research and preparation for module assessments


Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Short answer questions during the lectures and practical sessionsOngoing throughout the moduleAllOral
MCQ statistics tests20 problems1-12Written individual feedback on all questions

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Class test602 hours1-12Written individual feedback on all questions
Data analysis 110~10 problems1-12Written individual feedback on all questions
Data analysis 215~10 problems1-12Written individual feedback on all questions
Data analysis 315~10 problems1-12Written individual feedback on all questions


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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Class testExamination1-12August examination period
Data analysesData analyses1-12August examination period

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

  • Dytham, C. 2010. Choosing and using statistics: a biologist's guide. Wiley-Blackwell.
  • Crawley, M.J. 2005. Statistics: An introduction using R. Wiley.

Indicative learning resources - Web based and electronic resources

Module has an active ELE page

Key words search

Statistics, data collection, ANOVA, correlation, regression, probability, data distribution, variance, R programming language, graphics and experimental design

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date