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Statistics Explained
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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.

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.

Promotional Information

This book provides straightforward conceptual explanations of statistical methods for the life sciences, specially designed for students lacking a strong mathematical background.

About the Author

Steve McKillup is an Associate Professor of Biology in the School of Medical and Applied Sciences at Central Queensland University, Rockhampton. He has received several tertiary teaching awards, including the Vice-Chancellor's Award for Quality Teaching and an Australian Learning and Teaching Council citation 'for developing a highly successful method of teaching complex physiological and statistical concepts, and embodying that method in an innovative international textbook' (2008). He has gained a further citation for Outstanding Contributions to Student Learning, in the latest Australian Awards for University Teaching 2014. The citation has been awarded for 'developing resources that engage, empower and enable environmental science students to understand and use biostatistics', which includes his books on statistics that are being used worldwide. He is the author of Geostatistics Explained: An Introductory Guide for Earth Scientists (Cambridge, 2010).

Reviews

'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
"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 formula with graphics and realistic examples. He is a biologist writing for fellow-biologists. Although the book is structured like most other biostatistics texts, it 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 savor the clarity of the explanations of the common univariate and multivariate analyses. It would be an ideal core text for anyone teaching or studying biostatistics... I wish I had had a book like this when I started!" Andrew Boulton, Adjunct Professor, University of New England, New South Wales, 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-traveled item from my bookshelf to the shores of both undergraduate confusion and post-graduate clarification. However, I always make sure it comes back because it a valued item in my biology toolkit." Michael Kokkinn, Senior Lecturer in Biology, The 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, Associate Professor, University of Minnesota College of Pharmacy, USA
"I have no doubt that Statistics Explained will find a large and appreciative audience among undergraduate biology majors." Heather Lynch, The Quarterly Review of Biology

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