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Statistical And Data Handling Skills in Biology

Statistical And Data Handling Skills in Biology

Roland Ennos | Magnus Johnson

(2018)

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