Short description
Student-friendly introduction to statistics, essential for bioscience undergraduates, particularly those without a strong mathematical background.
Long description
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.
Review
'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.' JBE
Table of contents
- Preface
- Introduction
- 'Doing Science'
- hypotheses, experiments and disproof
- Collecting and displaying data
- Introductory concepts of experimental design
- Probability helps you make a decision about your results
- Working from samples
- data, populations and statistics
- Normal distributions
- test for comparing the means of one or two samples
- Type and Type error, power and sample size
- Single factor analysis of variance
- Multiple comparisons after ANOVA
- Two factor analysis of variance
- Important assumptions of analysis of variance
- transformations and a test for equality of variances
- Two factor analysis of variance without replication, and nested analysis of variance
- Relationships between variables
- linear correlation and linear regression
- Simple linear regression
- Non
- parametric statistics
- Non
- parametric tests for nominal scale data
- Non
- parametric tests for ratio, interval or ordinal scale data
- Choosing a test
- Doing science responsibly and ethically