INTRODUCTION

The design of experiments is one of the most important areas of microarray bioinformatics and is a long-standing topic in classical statistics. The reason for good experimental design is that it allows you to obtain maximum information from an experiment for minimum effort – which translates into time and money. The alternative to good experimental design is to performmicroarray experiments which produce data that cannot be analysed.

You might ask why it is that this topic appears at this point in the book, after data analysis rather than earlier in the book, alongside the material on the design of microarrays themselves. There are two reasons for this. The first is that the topics in this section use concepts from some of the earlier chapters, most importantly the ideas of hypothesis tests and p-values introduced in Chapter 7. But there is also amore philosophical reason why I have chosen to place the material on experimental design after the material on data analysis. In my view, it is absolutely critical to understand the scientific questions you are trying to answer, or even the scientific hypotheses you are seeking to generate, before you design your experiment. To this end, you should have a clear idea of the structure of the data you are seeking to produce and the types of data analysis you intend to employ before you design an experiment.

This chapter considers three areas of experimental design:

1. Section 10.2: Blocking, Randomisation and Blinding, looks at the statistical problems of confounding and bias, and the methods that are used to resolve these issues.

2. […]