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