Statistical And Data Handling Skills in Biology

Cover
Inside Front Cover
Title Page
Copyright Page
Dedication
Brief contents
Contents
List of figures and tables
Preface
Publisher’s acknowledgements
1 An introduction to statistics
	1.1 Becoming a biologist
	1.2 Awkward questions
	1.3 Why biologists have to repeat everything
	1.4 Why biologists have to bother with statistics
	1.5 Why statistical logic is so strange
	1.6 Why there are so many statistical tests
	1.7 Using the decision chart
	1.8 Using this text
2 Dealing with variability
	2.1 Introduction
	2.2 Examining the distribution of data
	2.3 The normal distribution
	2.4 Describing the normal distribution
	2.5 The variability of samples
	2.6 Confidence limits
	2.7 Presenting descriptive statistics and confidence limits
	2.8 Introducing computer programs
	2.9 Calculating descriptive statistics
	2.10 Self-assessment problems
3 Testing for normality and transforming data
	3.1 The importance of normality testing
	3.2 The Shapiro–Wilk test
	3.3 What to do if your data has a significantly different distribution from the normal
	3.4 Examining data in practice
	3.5 Transforming data
	3.6 The complete testing procedure
	3.7 Self-assessment problems
4 Testing for differences from an expected value or between two groups
	4.1 Introduction
	4.2 Why we need statistical tests for differences
	4.3 How we test for differences
	4.4 One- and two-tailed tests
	4.5 The types of t test and their non-parametric equivalents
	4.6 The one-sample t test
	4.7 The paired t test
	4.8 The two-sample t test
	4.9 Introduction to non-parametric tests for differences
	4.10 The one-sample sign test
	4.11 The Wilcoxon matched pairs test
	4.12 The Mann–Whitney U test
	4.13 Self-assessment problems
5 Testing for differences between more than two groups: ANOVA and its non-parametric equivalents
	5.1 Introduction
	5.2 One-way ANOVA
	5.3 Deciding which groups are different – post hoc tests
	5.4 Presenting the results of one-way ANOVAs
	5.5 Repeated measures ANOVA
	5.6 The Kruskal–Wallis test
	5.7 The Friedman test
	5.8 Two-way ANOVA
	5.9 The Scheirer–Ray–Hare Test
	5.10 Nested ANOVA
	5.11 Self-assessment problems
6 Investigating relationships
	6.1 Introduction
	6.2 Examining data for relationships
	6.3 Examining graphs
	6.4 Linear relationships
	6.5 Statistical tests for linear relationships
	6.6 Correlation
	6.7 Regression
	6.8 Studying common non-linear relationships
	6.9 Dealing with non-normally distributed data: rank correlation
	6.10 Self-assessment problems
7 Dealing with categorical data
	7.1 Introduction
	7.2 The problem of variation
	7.3 The x2 test for differences
	7.4 The x2 test for association
	7.5 Validity of x2 of tests
	7.6 Logistic regression
	7.7 Self-assessment problems
8 Designing experiments
	8.1 Introduction
	8.2 Preparation
	8.3 Excluding confounding variables
	8.4 Replication and pseudoreplication
	8.5 Randomisation and blocking
	8.6 Choosing the statistical test
	8.7 Choosing the number of replicates: power calculations
	8.8 Dealing with your results
	8.9 Self-assessment problems
9 More complex statistical analysis
	9.1 Introduction to complex statistics
	9.2 Experiments investigating several factors
	9.3 Experiments in which you cannot control all the variables
	9.4 Investigating the relationships between several variables
	9.5 Exploring data to investigate groupings
10 Presenting and writing about statistics
	10.1 Introduction – less is more!
	10.2 The introduction section
	10.3 The methods section
	10.4 The results section
	10.5 The discussion section
	10.6 The abstract or summary
Glossary
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Further reading
Solutions
Statistical tables
	Table S1: Critical values for the t statistic
	Table S2: Critical values for the correlation coefficient r
	Table S3: Critical values for the x2 statistic
	Table S4: Critical values for the Wilcoxon T distribution
	Table S5: Critical values for the Mann–Whitney U distribution
	Table S6: Critical values for the Friedman x2 distribution
	Table S7: Critical values for the Spearman rank correlation coefficient ρ
Index
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Inside Back Cover
Back Cover