DATA VISUALISATION Template for producing a diagram of a bacterial genome with the location of SNPs or other mutations, using DataGraph.
INTRODUCTORY BOOKS THAT I LIKE
Modern Statistics for The Life Sciences, by Alan Grafen & Rosie Hails. This book is a great introduction to ANOVA/regression-type stats, with a very useful focus on test assumptions and how to check them, and common pitfalls. The examples use Minitab, but the focus is very much on what tests to with data and how to think about this, rather than teaching the reader to use a specific software package.
Introductory Statistics with R, by Peter Dalgaard. This is a relatively short and well-indexed book that is easy to use as a quick reference. It's designed to help you implement statistical tests you already know and understand in R, i.e. it's geared towards to learning R code, not to learning the statistical tests themselves.
The following list of web resources will be regularly updated. Most of the links are aimed at undergraduate life sciences students, though there are some more advanced statistics resources as well. My top recommendation for stats Q&A is the stats stack exchange site CrossValidated.
Cohen, J (1990). Things I have learned (so far) American Psychologist 45(12), 1304–1312. This is a wonderful article about doing and using statistics which should be read by everyone. Full text is available here.
Unit of Analysis Issues in Laboratory-Based Research. This article deals with repeated measures in experiments. It covers the single-summary (take mean of repeats) and nested ANOVA / random effects approaches used by Grafen & Hails in their 'sheep' data example - but it goes into more detail and comes with the relevant R code.