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. Statistical and Data Handling Skills in Biology gives you everything you need to master the key skills of statistical analysis. 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. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit TheeBooks products do not have an expiry date. You will continue to access yourdigitalebookproducts whilst you have yourBookshelf installed.
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 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 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 r
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