We have nearly finished a new edition, which is a major rewrite. Apart from updating things and (we hope) explaining some topics more clearly, the big change is that we have used a linear modelling conceptual framework for most of the book, with most or many “conventional” statistical approaches introduced as particular flavours of linear models.
Statistical analysis is at the core of most modern biology, and many biological hypotheses, even deceptively simple ones, are matched by complex statistical models. Prior to the development of modern desktop computers, determining whether the data fit these complex models was the province of professional statisticians. Many biologists instead opted for simpler models whose structure had been simplified quite arbitrarily. Now, with immensely powerful statistical software available to most of us, these complex models can be fitted, creating a new set of demands & problems for biologists.
We need to:
- Know the pitfalls and assumptions of particular statistical models
- Be able to identify the type of model appropriate for the sampling design and kind of data that we plan to collect
- Be able to interpret the output of analyses using these models
- Be able to design experiments and sampling programs optimally, i.e. with the best possible use of our limited time and resources.
The analysis may be done by professional statisticians, rather than statistically trained biologists, especially in large research groups or multidisciplinary teams. In these situations, we need to be able to speak a common language –
- Frame our questions in such a way as to get a sensible answer
- Be aware of biological considerations that may cause statistical problems; we can not expect a statistician to be aware of the biological idiosyncrasies or our particular study, but if he or she lacks that information, we may get misleading or incorrect advice
- Understand the advice or analyses that we receive, and be able to translate that back into biology
This book aims to place biologists in a better position to do these things. It arose from our involvment in designing and analyzing our own data, but also providing advice to students and colleagues, and teaching classes in design and analysis. As part of these activities, we became aware, first of our limitations, prompting us to read more widely in the primary statistical literature, and second, and more importantly, of the complexity of the statistical models underlying much biological research. In particular, we continually encountered experimental designs that were not described comprehensively in many of our favourite texts. This book describes many of the common designs used in biological research, and we present the statistical models underlying those designs, with enough information to highlight their benefits and pitfalls.
Our emphasis here is on dealing with biological data – how to design sampling programs that represent the best use of our resources, how to avoid mistakes that make analyzing our data difficult, and how to analyse the data when they are collected. We emphasise the problems associated with real world biological situations.