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MRes Advanced Computer Science MRes

Year of entry: 2018

Course unit details:
Scientific Methods I - Scientific Evaluation, Experimental Design and Statistical Methods

Unit code COMP80131
Credit rating 5
Unit level FHEQ level 8 – doctoral degree
Teaching period(s) Semester 1
Offered by School of Computer Science
Available as a free choice unit? Yes


This course, referred to as 'Scientific Methods 1', considers the design of experiments and observational techniques for testing ideas that emerge from research in most areas of Computer Science. It is intended for all post-graduate research students.


Unit title Unit code Requirement type Description
Scientific Methods II - Fundamental Aspects of Research Methodology COMP80122 Co-Requisite Compulsory
Scientific Methods III - Academic Writing and Impact Studies COMP80142 Co-Requisite Compulsory


The main aim is to address the principles of experimental design and observation that underpin research in most areas of Computer Science. This requires fundamental issues of Scientific Methodology to be raised. The concepts of 'null hypothesis', hypothesis testing and the measurement of statistical significance must be addressed with a survey of statistical techniques and tools that are available. Evaluation methods ranging from subjective assessment, evaluations of software and formal statistical approaches will be introduced and illustrated by examples.

Learning outcomes

Learning outcomes are detailed on the COMP80131 course unit syllabus page on the School of Computer Science's website for current students.


The unit will have a series of lectures on experimental design, scientific evaluation and statistical methods, reinforced by illustrations and case-studies based on the experience of researchers in Computer Science and other areas of scientific research. Researchers within the CS School have been to provide examples of how research is evaluated in their particular research areas. Practical scenarios will be described and used to provide opportunities for designing experiments, looking at and evaluating data and developing evaluation strategies for representative problems.

Learning and Teaching Processes

There will be a series of 12 lectures reinforced by discussions on the illustrations, case-studies, assignments and software tools. There will be both individual and group-based practical discussions, including lab sessions for practising statistical software. The use of e-learning is not currently incorporated in this course, but materials for lectures and case-studies will be made electronically available.


Assessment will require the students to present a written assignment based on the lecture material and the results of some computer based experimental work and analysis.

Teaching and learning methods


12 lectures

Assessment methods

Method Weight
Written assignment (inc essay) 50%
Practical skills assessment 50%

Study hours

Independent study hours
Independent study 90

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.

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