Template for producing a diagram of a bacterial genome with the location of SNPs or other mutations, using DataGraph.

- 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.
- Experimental Design for the Life Sciences, by Graeme Ruxton and Nick Colegrave. What I love about this book is how it teaches a huge amount of really fundamental ideas in a concise way. Previous tutor groups liked the self-test questions and take-home messages sprinkled throughout the book .
- Maths from Scratch for Biologists, by Alan J. Cann. This is well worth reading if you don't have A-level maths, and also worth flicking through for revision purposes if you do.
- 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 MEI A-level statistics textbooks are very well written and clearly explain the key concepts using examples where appropriate.

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.

- HarvardX biomedical data science open online training. An integrated course that starts with the basics of populations, samples and hypothesis tests, then moves on to multivariate analyses and working with genomic data. Includes written material, videos and code.

- Review of 'The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives' by Stephen T. Ziliak and Deirdre N. McCloskey from
*Science* - How juries get fooled by statistics (TEDTalks) on YouTube
- Why P-values are Evil from Bob O'Hara's blog (originally published on on Nature Network)
- 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. - The link between error bars and statistical significance from GraphPad.com
- The PENIS of Statistics: a lecture by Andy Field on the key concepts of parameters, estimation, null hypothesis significance testing, intervals and standard error. (On YouTube).
- Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. A wonderful, concise article on the best ways to present data graphically in research papers.
- A protocol for data exploration to avoid common statistical problems - article by Zuur et al in
*Methods in Ecology & Evolution*.

- Java applets from
*Introduction to the Practice of Statistics*by Moore & McCabe. This is an absolutely wonderful interactive guide to statistical thinking, hypothesis tests and ANOVA. Start here! - Seeing Theory. Uses web applets to visualise how probability, sampling and statistical inference work.
- Interactive flash demo of linear regression that allows you to drag points around and see the effect on the fitted line. From the Catholic University of Louvain's Statistics eLearning Tools (in French).

- The Little Handbook of Statistical Practice by Gerard E. Dallal: a nice series of simple and to-the-point overviews of common statistical tests and statistical thinking, starting with the excellent Is Statistics Hard?
- Graham Hole's teaching resources for a stats module at Sussex University
- Wikipedia pages listing articles on articles on statistics, statistical tests and a table detailing which test to use when comparing means
- Statistics Hell-p is a comprehensive catalogue of resources produced by Andy Field of Sussex University
- HyperStat is an online statistics textbook which includes lots of exercises. I particularly recommend Chapter 9, the logic of hypothesis testing
- GraphPad have a library of articles on stats and data analysis.
- Statistics at Square One by TDV Swinscow & MJ Campbell (eBook provided by the BMJ)
- An Introduction to Non-parametric Statistics for Health Scientists (PDF from ResearchGate, you don;t need to sign up to download the file).
- ANOVA chapter from the Minitab User's Guide (PDF from Cambridge University)
- Non-Parametric Statistics by Bruce Weaver. (PDF)
- CAUSEweb – The Consortium for the Advancement of Undergraduate Statistics Education Includes links to a wide range of stats teaching resources on the web.
- Electronic Encyclopedia of Statistical Examples & Exercises
- 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.

- GraphPad QuickCalcs T-test, sign test, Fisher's exact test, ChiSq, CIs and lots more.
- Daniel Soper's Statistics Calculators A pretty comprehensive list of calculators for critical values, confidence intervals, effect sizes, regression, power and much more.
- Free Statistical Software - a list of freeware for Mac, Windows and Unix. Inlcudes general stats packages and also some programmes specifically designed for analysing animal populations.
- Probability calculator for Normal, t, F, Chi-squared and Binomial distributions.
- VassarStats particularly useful things here include Chi-Squared test with Monte Carlo simulation and a page of stats for clinical research]

- Veritas: the 'advanced' section of Andy Field's Statistics Hell-p
- Generalized Linear Models chapter from JJ Faraway's book
*Extending the Linear Model with R*.. A brief mathematical overview of GLMs and how they work. (Book details here). - Diagnosing problems in linear & generalised linear models
- StatNotes: Topics in Multivariate Analysis from North Carolina State University
- Multivariate Analysis of Variance (MANOVA) by Aaron French, John Poulsen, and Angela Yu.
- Leading statisticians respond to a psychology journal's move to ban NHST. From the Royal Statistical Society's StatsLife project.
- Getting started with meta-analysis. An article I published in
*Methods in Ecology & Evolution*, also discussed in this podcast & included in*MEE*'s June 2014 virtual issue Top Methods in Ecology and Evolution.