Statistical and Data Handling Skills in Biology

Statistical and Data Handling Skills in Biology

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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 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 choose 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 mastering it will make you a more rounded scientist.
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Product details

  • Paperback | 296 pages
  • 210.82 x 243.84 x 20.32mm | 589.67g
  • Pearson Education Limited
  • Harlow, United Kingdom
  • English
  • 3rd edition
  • 0273729497
  • 9780273729495
  • 301,110

Table of contents

1 An Introduction to Statistics1.1 Becoming a research biologist1.2 Awkward questions1.3 Why do biologists have to repeat everything?1.4 Why do biologists have to bother with statistics?1.5 Why is statistical logic so strange? 1.6 Why are there are so many statistical tests?1.7 Using the decision chart1.8 Using this book2 Dealing with variation2.1 Introduction2.2 Examining the distribution of data2.3 The normal distribution2.4 Describing the normal distribution2.5 The variability of samples 2.6 Confidence limits 2.7 Presenting descriptive statistics and confidence limits2.8 Introducing computer packages2.9 Calculating descriptive statistics2.10 Self assessment problems3 Testing for normality and transforming data3.1 The importance of normality testing3.2 The Kolgomorov-Smirnov test3.3 What to do if your data has a significantly different distribution from the normal3.4 Examining data in practice3.5 Transforming data3.6 The complete testing procedure3.7 Self-assessment problems4 Testing for differences between one or two groups4.1 Introduction4.2 Why we need statistical tests for differences4.3 How we test for differences4.4 One and two tailed tests4.5 The types of t test and their non parametric equivalents 4.6 The one sample t test4.7 The paired t test4.8 The two-samplet test4.9 Introduction to non parametric tests for differences4.10 The one sample sign test4.11 The Wilcoxon matched pairs test4.12 The Mann-Whitney U test4.13 Self assessment problems5 Testing for difference between more than two groups: ANOVA and its non parametric equivalents5.1 Introduction5.2 One way ANOVA5.3 Deciding which groups are different  post hoc tests5.4 Presenting the results of one way ANOVA s5.5 Repeated measures ANOVA5.6 The Kruskall-Wallis test5.7 The Friedman test5.8 Two way ANOVA5.9 The Scheirer-Ray-Hare test5.10 Nested ANOVA5.11 Self assessment problems6 Investigating relationships 6.1 Introduction6.2 Examining data for relationships6.3 Examining graphs6.4 Linear relationships6.5 Statistical tests for linear relationships6.6 Correlation6.7 Regression6.8 Studying common non-linear relationships6.9 Dealing with non normally distributed data: rank correlation6.10 Self assessment problems7 Dealing with Categorical Data7.1 Introduction7.2 The problem of variation7.3 The ÷2 test for differences7.4 The ÷2 test for associations7.5 Validity of ÷2 tests7.6 Logistic regression7.7 Self assessment problems8 Designing Experiments8.1 Introduction8.2 Preparation8.3 Excluding confounding variables8.4 Replication and pseudoreplication8.5 Randomisation and blocking8.6 Choosing the statistical test8.7 Choosing the number of replicates: power calculations8.8 Dealing with your results8.9 Self assessment problems9 More complex statistical analysis9.1 Introduction to complex statistics9.2 Experiments investigating several factors9.3 Experiments in which you cannot control all the variables9.4 Investigating the relationships between several variables9.5 Exploring data to investigate groupings10 Dealing with measurements and units10.1 Introduction10.2 Measuring10.3 Converting to SI units10.4 Combining values10.5 Expressing the answer10.6 Doing all three steps10.7 Constants and formulae10.8 Using calculations10.9 Logarithms, graphs and pH10.10 Self assessment problemsGlossaryFurther readingSolutionsStatistical TablesTable S1: Critical values for the t statisticTable S2: Critical values for the correlation coefficient rTable S3: Critical values for the c2statisticTable S4: Critical values for the Wilcoxon T statisticTable S5: Critical values for the Mann-Whitney U statisticTable S6: Critical values for the Friedman c2statisticTable S7: Critical values for the Spearman rank correlation coefficient r
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Review quote

'It is much improved by virtue of the addition of a second software to the supported platforms, and the layout too is greatly improved. The first chapter is excellent for demystifying stats which remains our greatest bugbear.' - Dr Andy Foggo, Marine Biology and Ecology Research Centre, University of Plymouth"a well-written, clearly-illustrated and user-friendly guidebook that explains the mysteries of biostatistics without either oversimplifying or scaring off first-year students with complex-looking equations. [...] There are lots of basic statistics textbooks out there, but this is one of the best." - Reviewed by Ana Claudia Mendes Malhado in The Biologist
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