Statistics Explained
Statistics Explained
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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, this book 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.
- Updated to include end-of-chapter exercises and refined explanations following extensive testing in undergraduate classes
- Uses life science examples throughout, remaining accessible even to those without a strong maths background
- Highly readable first person narrative encourages students who are apprehensive about statistics
Reviews & endorsements
'Every so often, a researcher or teacher comes across a book and exclaims 'I wish I had had a book like this when I started!' … Statistics Explained is such a book. Steve McKillup writes with empathy for students' anxiety about statistics. He replaces complex-looking formulae with graphics and realistic examples. He is a biologist writing for fellow-biologists … [The book] explains why the statistical test is needed before describing the test. Essential features of good survey and experimental design are clearly outlined … This is not 'just another biostatistics textbook'. Its sheer readability will restore confidence to the most anxious student while experienced researchers will savour the clarity of the explanations of the common univariate and multivariate analyses … an ideal core text for anyone teaching or studying biostatistics …' Andrew Boulton, University of New England, Australia
'It's remarkable that, after the appearance of many statistics textbooks and statistics computer packages over the years, finally someone has produced a succinct and accessible text that takes a common-sense and appealing approach to the basics of statistical analysis. Complementing Steve McKillup's remarkably lucid explanations is a format which sings pleasingly with clarity. The book progresses in logical fashion through the variety of statistical tests and gives the reader a sound background in the process without the common dizzying confusion. The narrative style and informative approach has made my copy a much-travelled item from my bookshelf to the shores of both undergraduate confusion and postgraduate clarification. However, I always make sure it comes back because it [is] a valued item in my biology toolkit.' Michael Kokkinn, University of South Australia
'Statistics Explained is an excellent introduction to statistics for new students and a helpful refresher for more seasoned researchers. The text is quite readable and filled with practical examples for the life sciences.' Erin D. Sheets, University of Minnesota College of Pharmacy
'Most exciting perhaps are the topics covered that are not often discussed in introductory textbooks … I have no doubt that Statistics Explained will find a large and appreciative audience among undergraduate biology majors.' The Quarterly Review of Biology
Product details
November 2011Paperback
9780521183284
416 pages
228 × 152 × 19 mm
0.66kg
199 b/w illus. 102 tables 51 exercises
Available
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. 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
- 17. 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
- References
- Index.
