Experimental Design and Data Analysis for Biologists

Description

Successful completion of BIOL209 is a pre-requisite for BIOL309, as the concepts covered
here lead on directly from those developed in the previous semester. BIOL309 is essential for
all students who intend to pursue postgraduate studies or go on to a career in any branch of
biological research.

Goal of the Course
The aim of BIOL309 is to build on the concepts developed in BIOL209 to provide training in
the use of advanced statistical techniques and in the design and analysis of biological
experiments. The biological focus applies both to the choice of relevant methods and the
specific examples discussed. The examples will cover a wide range of biology, from
biochemistry to ecology, although you should not expect every topic to be illustrated with an
example from your specific area of interest in biology. Note that one goal of the course is to
prepare students for postgraduate research programmes and jobs in research organisations, and
this affects the choice of course content and style.

The course covers data analysis, and emphases how familiar tests such as analysis of variance
and linear regression can be extended to provide a flexible suite of techniques which can be
applied to a variety of situations. This knowledge will be applied to the design of experiments,
covering concepts such as replication, power and repeated measures. An experiment can be
designed properly only on the basis of knowledge of the statistical test that will eventually be
required. This emphasis on the need to consider data analysis as an integral part of the
experimental design process means that topics will build on one another in sequence.

Learning Outcomes
By the end of this course, you should have achieved the following:
1. A clear understanding of a wide range of statistical tests, including analysis of
   variance, linear regression, non-parametric tests and generalised linear models;
2. Proficiency in the transcription and manipulation of data statistical packages;
3. A solid understanding of experimental design;
4. Proficiency in the analysis of a wide range of biological data.