This course introduces the key concepts and principles of experimental design, including orthogonality, bias, confounding, aliasing, blocking, randomisation, and replication.  A range of design types commonly used in field, laboratory, clinical and industrial experiments are discussed, including factorial and fractional factorial designs, Latin squares, blocked, repeated measures and crossover designs.

See our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk.

To make the most of this course, delegates should already have a working knowledge of multiple regression and analysis of variance (ANOVA).  Though not essential, it is recommended that delegates first attend either the “Survey Design & Analysis” stream of courses (see here and here) or “Quantitative Data Analysis” stream of courses (see here and here).

There is no formal assessment as part of this course.