Module Catalogue 2026/27

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

To introduce students to the idea that statistical thinking is not confined to the analysis of data but is vital to the way that scientific and other experiments are planned in relevant application areas like the medical sector, agriculture, industry and experimental science.

Module summary
It is well-known that experiments underpin much laboratory science and medicine, where clinical trials are a form of experiment involving human subjects. The approach is also used in areas less readily associated with experiments: industrial process industries have used them for many years and many web-based activities are just starting to exploit the experimental method. While the use of experiments is widely appreciated, the range and potential of different types of design, and their correct analysis, is not as widely disseminated. The course will describe the basic ideas of statistical design and introduce some of the more commonly used designs, together with the appropriate methods of analysis.

Outline Of Syllabus

Idea and role of randomization in treatment estimation, including use in clinical trials. Role of sample size in experimentation and sample size calculations (two group case only). Methods of randomization and the role of Bayesian ideas. Potential confounding factors and use of blocking and randomization to allow for them. Need for correct analysis.

Randomized block designs and their analysis, using matrix formulation and link with ANOVA. The idea of efficiency. More complex blocking patterns, including Latin squares and incomplete blocks. Treatment structures such as factorial designs.

Intended Knowledge Outcomes

At the end of the module it is expected that a student will be able to:

1.outline the principles of good statistical design, including randomisation and confounding.
2.appraise the main designs used in various branches of science, medicine, industry and experimental science.

Intended Skill Outcomes

At the end of the module it is expected that a student will be able to:

1. distinguish treatment effects from confounders and describe methods for efficiently eliminating the latter from treatment comparisons.
2. analyse appropriate experimental designs correctly.
3. Select when a factorial design might be used.

Students will develop skills across the cognitive domain (Bloom’s taxonomy, 2001 revised edition): remember, understand, apply, analyse, evaluate and create.

Teaching Rationale And Relationship

Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems.

The teaching methods are appropriate to allow students to develop a wide range of skills. From understanding basic concepts and facts to higher-order thinking.

Assessment Rationale And Relationship

A substantial formal unseen examination is appropriate for the assessment of the material in this module. The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and application of learning outcomes.

Examination problems may require a synthesis of concepts and strategies from different sections, while they may have more than one way for solution. The examination time allows the students to test different strategies, work out examples and gather evidence for deciding on an effective strategy, while carefully articulating their ideas and explicitly citing the theory they are using.

The coursework assignments allow the students to develop their problem-solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; the summative assessment has a secondary formative purpose as well as its primary summative purpose. Note: the exam for MAS8608 is more challenging than the exam for MAS3908.

General Notes

N/A