Choosing and Using Statistics: A Biologist's Guide

Front Cover
John Wiley & Sons, Apr 1, 2009 - Science - 264 pages
The first edition of this excellent handbook was extremely well received by both students and lecturers alike. It has helped to simplify the often complex and difficult task of choosing and using the right statistics package.

This is a book for any student or professional biologist who wants to process data using a statistical package on the computer, to select appropriate methods, and extract the important information from the often confusing output that is produced. It is aimed primarily at undergraduates and masters students in the biological sciences who have to apply statistics in practical classes and projects. Such users of statistics do not have to understand either how tests work or how to do the calculations, and these aspects are not covered in the book.

The new edition has been updated to cover the very latest versions of the computer packages described, expanded to include coverage for logistic regression, a more detailed consideration of multivariate analysis, data exploration and further examples of Principle Component Analysis and Discriminate Function Analysis are given.


  • New edition will use SPSS 10.0, Minitab 13.1 and Excel 2000.
  • New simplified version of the Key and flow chart of decisions to reach simple statistical tests.
  • Section on multivariate techniques expanded to give further examples of PCA and DFA.
  • Aimed at students using statistics for projects and in practical classes.
  • Statistical jargon explained through an extensive glossary and key to symbols.
  • Stresses the importance of experimental design, measurement of data and interpretation of results rather than an understanding of the statistical tests themselves.
 

Contents

1 Eight steps to successful data analysis
1
2 The basics
2
Pvalues
3
Experiments
4
Tests of difference
5
Tests for data investigation
6
3 Choosing a test a key
7
A key to assist in your choice of statistical test
8
Repeated measures ANOVA
107
Independent samples
108
Oneway ANOVA
109
Post hoc testing after oneway ANOVA
116
KruskalWallis test
119
Post hoc testing after the KruskalWallis test
121
There are two independent ways of classifying the data
122
Twoway ANOVA without replication
126

4 Hypothesis testing sampling and experimental design
22
Pvalues
23
Sampling
24
Number of sample units
25
Timing of sampling
26
Procedural controls
27
Statistical control
28
5 Statistics variables and distributions
30
Types of statistics
31
Types of variables or scales of measurement
32
Ranked variables
33
Types of distributions
34
Binomial distribution
35
Negative binomial distribution
37
Continuous distributions
38
Describing normal distribution further
39
Transformations
41
tdistribution
43
Chisquare distribution
44
Ranking quartiles and the interquartile range
45
6 Descriptive and presentational techniques
46
Descriptive statistics
49
Statistics of distribution dispersion or spread
50
Other summary statistics
53
Displaying data summarizing two or more variables
57
Displaying data comparing two variables
58
Trends predictions and time series
59
Displaying data comparing more than two variables
63
Multiple trends time series and predictions
64
7 The tests 1 tests to look at differences
66
Chisquare test 2
68
KolmogorovSmirnov test
77
AndersonDarling test
79
Do the observations from two groups differ?
80
Wilcoxon signed ranks test
83
Sign test
85
Unpaired data
89
Oneway ANOVA
94
MannWhitney U test
101
Do the observations from more than two groups differ?
104
More than one observation for each factor combination with replication
133
Twoway ANOVA with replication
137
ScheirerRayHare test
145
There are more than two independent ways to classify the data
151
Threeway ANOVA with replication
152
Multiway ANOVA
157
Not all classifications are independent
158
Nested or hierarchical designs
159
Twolevel nested design ANOVA
160
8 The tests 2 tests to look at relationships
165
Cramér coefficient of association
172
Observations assigned a value
173
Spearman rankorder correlation
177
Kendall rankorder correlation
179
Regression
181
Kendall robust linefit method
189
Model II regression
192
Tests for more than two variables
193
Partial correlation
194
Analysis of covariance ANCOVA
195
Multiple regression
198
Path analysis
199
9 The tests 3 tests for data exploration
200
Canonical variate analysis CVA
205
Multivariate analysis of variance MANOVA
210
Multivariate analysis of covariance MANCOVA
212
DECORANA and TWINSPAN
214
10 Symbols and letters used in statistics
216
Upper case letters
217
Lower case letters
218
11 Glossary
219
12 Assumptions of the tests
234
What if the assumptions are violated?
236
13 Hints and tips
237
Sampling
238
Displaying the data
239
14 A table of statistical tests
241
Index
243
Copyright

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About the author (2009)

Calvin Dytham has a wide range of research interests in ecology and evolutionary biology and is especially interested in the impacts of dispersal and the arrangement of individuals in space on ecological and evolutionary processes. He is a Reader in the Department of Biology at the University of York, UK, and has been teaching statistics to undergraduate and postgraduate students since 1994.

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