403 pages, b/w illustrations, tables
An understanding of statistics and experimental design is essential for life science studies, but many students lack a mathematical background and some even dread taking an introductory statistics course. Using a refreshingly clear and encouraging reader-friendly approach, Statistics Explained: An Introductory Guide for Life Scientists helps students understand how to choose, carry out, interpret and report the results of complex statistical analyses, critically evaluate the design of experiments and proceed to more advanced material.
Taking a straightforward conceptual approach, it is specifically designed to foster understanding, demystify difficult concepts and encourage the unsure. Even complex topics are explained clearly, using a pictorial approach with a minimum of formulae and terminology. Examples of tests included throughout are kept simple by using small data sets. In addition, end-of-chapter exercises, new to this edition, allow self-testing. Handy diagnostic tables help students choose the right test for their work and remain a useful refresher tool for postgraduates.
"McKillup deserves to be congratulated on having produced a clear and accessible statistics book pitched at the uninitiated or the unsure. The slightly panicky should relax and take a quiet dose of a couple of chapters at a time and pretty soon all should seem much less awful. For the novice, confident or not, Statistics Explained offers an excellent primer that does not purport to be fully comprehensive yet manages to cover most things one really needs to know."
- Ian C. W. Hardy, University of Nottingham
"This book would be an excellent course textbook to be used as an accompaniment to an undergraduate (or postgraduate) course in statistics and experimental design (either by students, or as a basis for teaching [...] There are distinct strengths to this book. the procedure adopted for statistical tests remains consistent throughout the book. The chapters are built up skilfully, the order of ideas being entirely appropriate. These ideas are 'sign-posted' with carefully chosen sub-headings, and the explanations carefully crafted to focus on students' understanding, rather than simply enabling them to mechanically number-crunch, avoiding excessive mathematical terminology."
2. Doing science: hypotheses, experiments and disproof
3. Collecting and displaying data
4. Introductory concepts of experimental design
5. Doing science responsibly and ethically
6. Probability helps you make a decision about your results
7. Probability explained
8. Using the normal distribution to make statistical decisions
9. Comparing the means of one and two samples of normally distributed data
10. Type 1 and Type 2 error, power and sample size
11. Single factor analysis of variance
12. Multiple comparisons after ANOVA
13. Two-factor analysis of variance
14. Important assumptions of analysis of variance, transformations and a test for equality of variances
15. More complex ANOVA
16. Relationships between variables: correlation and regression
18. Analysis of covariance
19. Non-parametric statistics
20. Non-parametric tests for nominal scale data
21. Non-parametric tests for ratio, interval or ordinal scale data
22. Introductory concepts of multivariate analysis
23. Choosing a test
Appendix: critical values of chi-square, t and F
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Steve McKillup is an Associate Professor of Biology in the School of Biological and Environmental Sciences at Central Queensland University, Rockhampton.