Additional Information
Book Details
Abstract
Is there a link between people’s heart rate and blood pressure?
Does the lead in petrol fumes affect the growth of roadside plants?
The ability to expertly analyse statistical data is a crucial skill in the biological sciences – it is fundamental to fully understanding what your experiments are actually telling you and so being able to answer your research questions.
Statistical and Data Handling Skills in Biology gives you everything you need to understand and use statistical tests within your studies and future independent research.
Written in a straight-forward and easy to understand style it presents all of the tests you will need throughout your studies, and shows you how to select the right tests to get the most out of your experiments. All of this is done in the context of biological examples so you can see just how relevant a skill this is, and how becoming fully proficient will make you a more rounded scientist.
This 4th edition has been thoroughly updated throughout and now includes detailed coverage of the free statistical package R studio and a new chapter on how to write about and present statistics in papers, theses and reports. The first chapter has also been revised to introduce students to the need for and ideas behind statistical analysis.
Features
· Clear explanation with step by step detail of how to carry out a wide range of statistical analyses will help you to quickly gain understanding and confidence in this essential area.
· Useful decision charts will help you to select the right statistical test and gain confidence in answering your research questions.
· Real world examples in each chapter will help you to develop an applied understanding of the full range of statistical techniques
· Self-assessment problems scenarios at the end of each chapter enable you to practice applying your understanding of a technique, thereby improving your confidence in using numbers. Guided answers allow you to check your understanding.
Statistical and Data Handling Skills in Biology 4th edition is ideal for any biomedic or environmental scientist getting to grips with statistical analysis for use in class on as part of independent study.
Table of Contents
Section Title | Page | Action | Price |
---|---|---|---|
Cover | Cover | ||
Inside Front Cover | IFC | ||
Title Page | iii | ||
Copyright Page | iv | ||
Dedication | v | ||
Brief contents | vii | ||
Contents | ix | ||
List of figures and tables | xiii | ||
Preface | xvii | ||
Publisher’s acknowledgements | xix | ||
1 An introduction to statistics | 1 | ||
1.1 Becoming a biologist | 1 | ||
1.2 Awkward questions | 2 | ||
1.3 Why biologists have to repeat everything | 2 | ||
1.4 Why biologists have to bother with statistics | 3 | ||
1.5 Why statistical logic is so strange | 4 | ||
1.6 Why there are so many statistical tests | 5 | ||
1.7 Using the decision chart | 6 | ||
1.8 Using this text | 8 | ||
2 Dealing with variability | 10 | ||
2.1 Introduction | 10 | ||
2.2 Examining the distribution of data | 10 | ||
2.3 The normal distribution | 13 | ||
2.4 Describing the normal distribution | 16 | ||
2.5 The variability of samples | 17 | ||
2.6 Confidence limits | 19 | ||
2.7 Presenting descriptive statistics and confidence limits | 21 | ||
2.8 Introducing computer programs | 22 | ||
2.9 Calculating descriptive statistics | 28 | ||
2.10 Self-assessment problems | 31 | ||
3 Testing for normality and transforming data | 33 | ||
3.1 The importance of normality testing | 33 | ||
3.2 The Shapiro–Wilk test | 33 | ||
3.3 What to do if your data has a significantly different distribution from the normal | 36 | ||
3.4 Examining data in practice | 37 | ||
3.5 Transforming data | 39 | ||
3.6 The complete testing procedure | 43 | ||
3.7 Self-assessment problems | 43 | ||
4 Testing for differences from an expected value or between two groups | 44 | ||
4.1 Introduction | 44 | ||
4.2 Why we need statistical tests for differences | 44 | ||
4.3 How we test for differences | 45 | ||
4.4 One- and two-tailed tests | 47 | ||
4.5 The types of t test and their non-parametric equivalents | 47 | ||
4.6 The one-sample t test | 47 | ||
4.7 The paired t test | 52 | ||
4.8 The two-sample t test | 58 | ||
4.9 Introduction to non-parametric tests for differences | 65 | ||
4.10 The one-sample sign test | 65 | ||
4.11 The Wilcoxon matched pairs test | 70 | ||
4.12 The Mann–Whitney U test | 75 | ||
4.13 Self-assessment problems | 80 | ||
5 Testing for differences between more than two groups: ANOVA and its non-parametric equivalents | 83 | ||
5.1 Introduction | 83 | ||
5.2 One-way ANOVA | 84 | ||
5.3 Deciding which groups are different – post hoc tests | 90 | ||
5.4 Presenting the results of one-way ANOVAs | 94 | ||
5.5 Repeated measures ANOVA | 95 | ||
5.6 The Kruskal–Wallis test | 102 | ||
5.7 The Friedman test | 107 | ||
5.8 Two-way ANOVA | 112 | ||
5.9 The Scheirer–Ray–Hare Test | 118 | ||
5.10 Nested ANOVA | 123 | ||
5.11 Self-assessment problems | 129 | ||
6 Investigating relationships | 132 | ||
6.1 Introduction | 132 | ||
6.2 Examining data for relationships | 132 | ||
6.3 Examining graphs | 133 | ||
6.4 Linear relationships | 133 | ||
6.5 Statistical tests for linear relationships | 135 | ||
6.6 Correlation | 135 | ||
6.7 Regression | 144 | ||
6.8 Studying common non-linear relationships | 150 | ||
6.9 Dealing with non-normally distributed data: rank correlation | 155 | ||
6.10 Self-assessment problems | 160 | ||
7 Dealing with categorical data | 163 | ||
7.1 Introduction | 163 | ||
7.2 The problem of variation | 163 | ||
7.3 The x2 test for differences | 165 | ||
7.4 The x2 test for association | 170 | ||
7.5 Validity of x2 of tests | 177 | ||
7.6 Logistic regression | 178 | ||
7.7 Self-assessment problems | 184 | ||
8 Designing experiments | 186 | ||
8.1 Introduction | 186 | ||
8.2 Preparation | 187 | ||
8.3 Excluding confounding variables | 187 | ||
8.4 Replication and pseudoreplication | 187 | ||
8.5 Randomisation and blocking | 189 | ||
8.6 Choosing the statistical test | 191 | ||
8.7 Choosing the number of replicates: power calculations | 193 | ||
8.8 Dealing with your results | 200 | ||
8.9 Self-assessment problems | 200 | ||
9 More complex statistical analysis | 203 | ||
9.1 Introduction to complex statistics | 203 | ||
9.2 Experiments investigating several factors | 204 | ||
9.3 Experiments in which you cannot control all the variables | 204 | ||
9.4 Investigating the relationships between several variables | 208 | ||
9.5 Exploring data to investigate groupings | 211 | ||
10 Presenting and writing about statistics | 213 | ||
10.1 Introduction – less is more! | 213 | ||
10.2 The introduction section | 213 | ||
10.3 The methods section | 214 | ||
10.4 The results section | 214 | ||
10.5 The discussion section | 217 | ||
10.6 The abstract or summary | 218 | ||
Glossary | 219 | ||
A | 219 | ||
B | 219 | ||
C | 219 | ||
D | 219 | ||
E | 220 | ||
F | 220 | ||
G | 220 | ||
I | 220 | ||
L | 220 | ||
M | 220 | ||
N | 220 | ||
P | 220 | ||
Q | 221 | ||
R | 221 | ||
S | 221 | ||
T | 221 | ||
V | 222 | ||
Further reading | 223 | ||
Solutions | 224 | ||
Statistical tables | 245 | ||
Table S1: Critical values for the t statistic | 245 | ||
Table S2: Critical values for the correlation coefficient r | 246 | ||
Table S3: Critical values for the x2 statistic | 247 | ||
Table S4: Critical values for the Wilcoxon T distribution | 248 | ||
Table S5: Critical values for the Mann–Whitney U distribution | 250 | ||
Table S6: Critical values for the Friedman x2 distribution | 251 | ||
Table S7: Critical values for the Spearman rank correlation coefficient ρ | 253 | ||
Index | 254 | ||
A | 254 | ||
B | 254 | ||
C | 254 | ||
D | 255 | ||
E | 255 | ||
F | 255 | ||
G | 255 | ||
H | 255 | ||
I | 255 | ||
K | 255 | ||
L | 255 | ||
M | 255 | ||
N | 255 | ||
O | 256 | ||
P | 256 | ||
Q | 256 | ||
R | 256 | ||
S | 257 | ||
T | 258 | ||
U | 258 | ||
V | 258 | ||
W | 258 | ||
Y | 258 | ||
Z | 258 | ||
Inside Back Cover | IBC | ||
Back Cover | Back Cover |