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This is an archived syllabus from 2019-2020

COMP80131 Scientific Methods I - Scientific Evaluation, Experimental Design and Statistical Methods syllabus 2019-2020

COMP80131 Scientific Methods I - Scientific Evaluation, Experimental Design and Statistical Methods

Postgraduate Research
Credits: 5
Enrolled students: 51

Course lecturers: Simon Harper

Jonathan Shapiro

Additional staff: view all staff


  • Co-Requisite (Compulsory): COMP80122
  • Co-Requisite (Compulsory): COMP80142

Assessment methods

  • 50% Coursework
  • 50% Practical skills assessment
Sem 1 w7-12 Lecture G41 Tue 11:00 - 12:00 -
Sem 1 w7-12 Lecture 2.19 Fri 13:00 - 14:00 -


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.


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.


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 methods


12 lectures

Study hours

Learning outcomes

On successful completion of this unit, a student will be able to:

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

Reading list

No reading list found for COMP80131.

Additional notes

Course unit materials

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