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Statistics Explained is a reader-friendly introduction to experimental design and statistics for undergraduate students in the life sciences, particularly those who do not have a strong mathematical background. Hypothesis testing and experimental design are discussed first. Statistical tests are then explained using pictorial examples and a minimum of formulae. This class-tested approach, along with a well-structured set of diagnostic tables will give students the confidence to choose an appropriate test with which to analyse their own data sets. Presented in a lively and straight-forward manner, Statistics Explained will give readers the depth and background necessary to proceed to more advanced texts and applications. It will therefore be essential reading for all bioscience undergraduates, and will serve as a useful refresher course for more advanced students. Table Of Contents Preface 1. Introduction 2. 'Doing Science' - hypotheses, experiments and disproof 3. Collecting and displaying data 4. Introductory concepts of experimental design 5. Probability helps you make a decision about your results 6. Working from samples - data, populations and statistics 7. Normal distributions - test for comparing the means of one or two samples 8. Type 1 and Type 2 error, power and sample size 9. Single factor analysis of variance 10. Multiple comparisons after ANOVA 11. Two factor analysis of variance 12. Important assumptions of analysis of variance: transformations and a test for equality of variances 13. Two factor analysis of variance without replication, and nested analysis of variance 14. Relationships between variables: linear correlation and linear regression 15. Simple linear regression 16. Non-parametric statistics 17. Non-parametric tests for nominal scale data 18. Non-parametric tests for ratio, interval or ordinal scale datav 19. Choosing a test 20. Doing science responsibly and ethically.