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2022/23 Undergraduate Module Catalogue

BLGY2192 Experimental Design and Analysis

10 creditsClass Size: 200

Module manager: Dr Anna Riach

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2022/23

This module is not approved as a discovery module


Students should be able to design good experiments for field and laboratory use and use SPSS to use a wide variety of statistical techniques for the analysis and presentation of complex multifactorial datasets.

Learning outcomes
On completion of the module students will be able to:
- Demonstrate a broad understanding of the concepts behind the collection, management, analysis and interpretation of numerical data;
- Design an appropriate experiment or survey to collect numercial data in a range of different biological contexts;
- Understand the idea of testing hypotheses using the probabilities of the hypotheses being incorrect;
- Analyse and interpret numerical data using techniques including exploratory data analysis; correlation; regression; chi-square; analysis of variance; analysis of covariance, multivariate analysis and other statistical techniques;
- Present data analysis and interpretation in the context of scientific reports.

Skills outcomes
- Team working
- problem solving
- data analysis and interpretation
- scientific reporting
- quality assurance


The course will consolidate existing techniques and develop new understanding in experimental design and analysis from level 1 through the use of research-based case studies. The focus will be on understanding how different areas of research that are pursued across the School of Biology require different statistical and scientific approaches. In addition to the design of experiments and analysis of data, we will also cover the ethical aspects of different types of research and the communication of findings.

Upon completion of the module, students should be able to design good experiments for field and laboratory use in a range of different areas of biology and use SPSS to apply a wide variety of statistical techniques for the analysis and presentation of complex datasets. There will also be opportunities to learn the programming language R, which is the tool used for statistical analysis in many areas of biological research.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours79.50
Total Contact hours20.50
Total hours (100hr per 10 credits)100.00

Private study

- 40 hours: working on exercises in own time (5 x 8 hours)
- 39.5 hours: revision and background reading.

Opportunities for Formative Feedback

Computer based report: individual reports on a data analysis problem will be introduced in week 3 and all information presented in week 6. Students can work together to undertake statistical analysis, but the actual reports are individual. Feedback will be given via written and video feedback on Minerva.

Methods of assessment

Assessment typeNotes% of formal assessment
Reportcomputer based report to undertake statistical analysis and individual write-up30.00
In-course MCQFormative MCQ tests: 6 x in-course on-line MCQs tests (week 2, 3, 4, 5, 6 and 7).0.00
Total percentage (Assessment Coursework)30.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated.

Exam typeExam duration% of formal assessment
Online Time-Limited assessment3 hr 00 mins70.00
Total percentage (Assessment Exams)70.00

Past exam papers can be found on-line at the Examinations section of Student Services.

Reading list

The reading list is available from the Library website

Last updated: 09/12/2022


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