258 pages, b/w illustrations, tables
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.
Reviews of the previous edition:
"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
1 An introduction to statistics
2 Dealing with variability
3 Testing for normality and transforming data
4 Testing for differences from an expected value or between two groups
5 Testing for differences between more than two groups: ANOVA and its non-parametric equivalents
6 Investigating relationships
7 Dealing with categorical data
8 Designing experiments
9 More complex statistical analysis
10 Presenting and writing about statistics
Table S1: Critical values for the t statistic
Table S2: Critical values for the correlation coefficient r
Table S3: Critical values for the χ2 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 χ2 distribution
Table S7: Critical values for the Spearman rank correlation coefficient r
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