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Module Reading List

Experimental Design and Analysis, 2021/22, Semester 1
Dr Chris Hassall
Tutor information is taken from the Module Catalogue

There is no single text for the module. The course will be taught using a variety of case studies based on cutting edge research from researchers within the School of Biology. However, there are a number of very useful texts that offer support for particular aspects of statistical analysis and experimental design. I have tried to focus on those books that are available online, and other books are listed below (some of which are better, but only available in the library) for those who are interested.

Online Texts

Ennos, R. & Johnson, M. (2018) Statistical and data handling skills in biology. 4th Edition. Prentice Hall. Short and to the point.

Van Emden, H. (2008). Statistics for terrified biologists. Blackwell. Good, approachable, and covers experimental design

Dytham, C. (2011). Choosing and using statistics: a biologist's guide. 3rd Edition. Wiley-Blackwell. A tried and tested book that covers the course well. Incurred statistical wrath for an incorrect definition of a p-value on page 3 (it is *not* the probability that the null hypothesis is correct!), but otherwise good.

Beckerman, Petchy (2012) Getting Started with R: An introduction for Biologists, OUP. I will bang on about R throughout the course, and this is a great introduction. The 2017 Ed is available in the library in hard copy.

Gaubatz (2015) A survivor's guide to R : an introduction for the uninitiated and the unnerved, SAGE. Not a book I have a lot of experience with and it isn't biology-focused, but supposedly a good intro to R.

Hard Copy Only Texts

Andy Field (2018) Discovering statistics using IBM SPSS statistics  5th Edition, Sage. An excellent book with clear, engaging, and funny explanations. Older editions will still be fine, but some sections will be out of date in terms of which menus to use. There is also an R version: Field, Miles & Field (2012) Discovering Statistics Using R, Sage. 

Cronk, B.C. (2018). How to use SPSS : a step-by-step guide to analysis and interpretation ISBN: 9781138308534 (pbk.); 9781138308541 (hardback); 9781315142999 (ebook), 10th Edition. Routledge. A rather terse volume, tells you what to do rather than why.A new edition is out soon.

Holmes, S & Huber, W. (2019) Modern statistics for modern biology. Cambridge. More advanced than this course, covers analysis of big data using R. If you are going into bioinformatics etc, do check this book out.

Huff, D. (1991) How to lie with statistics, Penguin. The Huff book is easy and fun to read, and addresses abuse of graphs etc, but is getting on a bit. 

There's then books that put stats into a scientific context

Mead, R., Curnow, R.N. & Hasted, A.M. (2002). Statistical methods in agriculture and experimental biology. 3rd edition. Chapman and Hall. A classic, old school, presentation of agricultural-style experimental design. 

Field, A. & Hole, G. (2003). How to design and report experiments Sage.

Barnard, C., Golbert, F. & McGregor, P. (2011). Asking questions in biology. 4th edition. Pearson.

Finally, see also the role of statistics and big data in our changing lives, for example

Zuboff S. (2019) The Age of Surveillance Capitalism. Profile books. Scary, hard going, but essential reading to understand how data are transforming our lives and societies.

This list was last updated on 02/07/2021