What is included with this book?
Banish your fears of statistical analysis using this clearly written and highly successful textbook. Statistics for Veterinary and Animal Science Third Edition is an introductory text which assumes no previous knowledge of statistics. It starts with very basic methodology and builds on it to encompass some of the more advanced techniques that are currently used. This book will enable you to handle numerical data and critically appraise the veterinary and animal science literature. Written in a non-mathematical way, the emphasis is on understanding the underlying concepts and correctly interpreting computer output, and not on working through mathematical formulae.
Key features:
New to this edition:
Carrying out statistical procedures and interpreting the results is an integral part of veterinary and animal science. This is the only book on statistics that is specifically written for veterinary science and animal science students, researchers and practitioners.
Aviva Petrie, Head of Biostatistics Unit and Senior Lecturer, UCL Eastman Dental Institute, London; Honorary Lecturer in Medical Statistics, Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK. She is also author of a number of other books, including Medical Statistics at a Glance.
Paul Watson is a distinguished and well respected scientist in the field of Reproductive Biology, and is Emeritus Professor at the Royal Veterinary College, UK.
Preface to Third Edition ix
Preface to First Edition xi
Preface to Second Edition xiii
1 The Whys and Wherefores of Statistics 1
1.1 Learning objectives 1
1.2 Aims of the book 1
1.3 What is statistics? 2
1.4 Statistics in veterinary and animal science 3
1.5 Evidence-based veterinary medicine 4
1.6 Types of variable 4
1.7 Variations in measurements 5
1.8 Terms relating to measurement quality 7
1.9 Populations and samples 9
1.10 Types of statistical procedures 10
1.11 Conclusion 10
Exercises 10
2 Descriptive Statistics 12
2.1 Learning objectives 12
2.2 Summarizing data 12
2.3 Empirical frequency distributions 12
2.4 Tables 14
2.5 Diagrams 15
2.6 Numerical measures 19
2.7 Reference interval 24
Exercises 25
3 Probability and Probability Distributions 28
3.1 Learning objectives 28
3.2 Probability 28
3.3 Probability distributions 30
3.4 Discrete probability distributions 31
3.5 Continuous probability distributions 33
3.6 Relationships between distributions 42
Exercises 43
4 Sampling and Sampling Distributions 46
4.1 Learning objectives 46
4.2 Distinction between the sample and the population 46
4.3 Statistical inference 46
4.4 Sampling distribution of the mean 48
4.5 Confidence interval for a mean 50
4.6 Sampling distribution of the proportion 52
4.7 Confidence interval for a proportion 53
4.8 Bootstrapping and jackknifing 53
Exercises 54
5 Experimental Design and Clinical Trials 55
5.1 Learning objectives 55
5.2 Types of study 55
5.3 Introducing clinical trials 59
5.4 Importance of design in the clinical trial 60
5.5 Control group 61
5.6 Assignment of animals to the treatment groups 62
5.7 Avoidance of bias in the assessment procedure 65
5.8 Increasing the precision of the estimates 66
5.9 Further considerations 68
Exercises 73
6 An Introduction to Hypothesis Testing 75
6.1 Learning objectives 75
6.2 Introduction 75
6.3 Basic concepts of hypothesis testing 75
6.4 Type I and Type II errors 79
6.5 Distinction between statistical and biological significance 80
6.6 Confidence interval approach to hypothesis testing 81
6.7 Collecting our thoughts on confidence intervals 82
6.8 Equivalence and non-inferiority studies 82
Exercises 83
7 Hypothesis Tests 1. The t-test: Comparing One or Two Means 85
7.1 Learning objectives 85
7.2 Requirements for hypothesis tests for comparing means 85
7.3 One-sample t-test 87
7.4 Two-sample t-test 89
7.5 Paired t-test 92
Exercises 96
8 Hypothesis Tests 2. The F-test: Comparing Two Variances or More Than Two Means 100
8.1 Learning objectives 100
8.2 Introduction 100
8.3 The F-test for the equality of two variances 100
8.4 Levene’s test for the equality of two or more variances 102
8.5 Analysis of variance (ANOVA) for the equality of means 102
8.6 One-way analysis of variance 105
Exercises 109
9 Hypothesis Tests 3. The Chi-squared Test: Comparing Proportions 112
9.1 Learning objectives 112
9.2 Introduction 112
9.3 Testing a hypothesis about a single proportion 112
9.4 Comparing two proportions: independent groups 113
9.5 Testing associations in an r × c contingency table 117
9.6 Comparing two proportions – paired observations 120
9.7 Chi-squared goodness-of-fit test 122
Exercises 123
10 Linear Correlation and Regression 126
10.1 Learning objectives 126
10.2 Introducing linear correlation and regression 126
10.3 Linear correlation 127
10.4 Simple (univariable) linear regression 132
10.5 Regression to the mean 142
Exercises 142
11 Further Regression Analyses 146
11.1 Learning objectives 146
11.2 Introduction 146
11.3 Multiple linear regression 147
11.4 Multiple logistic regression: a binary response variable 154
11.5 Poisson regression 159
11.6 Regression methods forclustered data 161
Exercises 163
12 Non-parametric Statistical Methods 165
12.1 Learning objectives 165
12.2 Parametric and non-parametric tests 165
12.3 Sign test 167
12.4 Wilcoxon signed rank test 169
12.5 Wilcoxon rank sum test 171
12.6 Non-parametric analyses of variance 173
12.7 Spearman’s rank correlation coefficient 175
Exercises 178
13 Further Aspects of Design and Analysis 181
13.1 Learning objectives 181
13.2 Transformations 181
13.3 Sample size 184
13.4 Sequential and interim analysis 189
13.5 Meta-analysis 190
13.6 Methods of sampling 194
Exercises 198
14 Additional Techniques 200
14.1 Learning objectives 200
14.2 Diagnostic tests 200
14.3 Bayesian analysis 208
14.4 Measuring agreement 211
14.5 Measurements at successive points in time 218
14.6 Survival analysis 221
14.7 Multivariate analysis 226
Exercises 227
15 Some Specialized Issues and Procedures 230
15.1 Learning objectives 230
15.2 Introduction 230
15.3 Ethical and legal issues 230
15.4 Spatial statistics and geospatial information systems 233
15.5 Veterinary surveillance 237
15.6 Molecular and quantitative genetics 240
Exercises 242
16 Evidence-based Veterinary Medicine 243
16.1 Learning objectives 243
16.2 Introduction 243
16.3 What is evidence-based veterinary medicine? 244
16.4 Why has evidence-based veterinary medicine developed? 244
16.5 What is involved in practising evidence-based veterinary medicine? 245
16.6 Integrating evidence-based veterinary medicine into clinical practice 249
16.7 Example 249
Exercises 250
17 Reporting Guidelines 252
17.1 Learning objectives 252
17.2 Introduction to reporting guidelines (EQUATOR network) 252
17.3 REFLECT statement 254
17.4 ARRIVE guidelines (research using laboratory animals) 255
17.5 STROBE guidelines
(observational studies) 255
17.6 STARD statement (diagnostic accuracy) 262
17.7 PRISMA statement (systematic reviews and meta-analysis) 265
18 Critical Appraisal of Reported Studies 269
18.1 Learning objectives 269
18.2 Introduction 269
18.3 A template for critical appraisal of published research involving animals 270
18.4 Paper 1 273
18.5 Critical appraisal of paper 1 284
18.6 Paper 2 288
18.7 Critical appraisal of paper 2 297
18.8 General conclusion 302
Solutions to Exercises 303
Appendices 331
A Statistical Tables 331
B Tables of Confidence Intervals 347
C Glossary of Notation 349
D Glossary of Terms 353
E Flowcharts for Selection of Appropriate Tests 376
References 377
Index 379