Provides an excellent introductory text for students on the principles and methods of statistical analysis in the life sciences, helping them choose and analyse statistical tests for their own problems and present their findings.

 

An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real-life" situations such as collecting insects with a sweep net or counting seagulls on a cliff face can hardly be expected to be as reliable or controllable as a laboratory-based experiment. Acknowledging the peculiarites of field-based data and its interpretation, this book provides a superb introduction to statistical analysis helping students relate to their particular and often diverse data with confidence and ease.

 

To enhance the usefulness of this book, the new edition incorporates the more advanced method of multivariate analysis, introducing the nature of multivariate problems and describing the the techniques of principal components analysis, cluster analysis and discriminant analysis which are all applied to biological examples. An appendix detailing the statistical computing packages available has also been included.

 

It will be extremely useful to undergraduates studying ecology, biology, and earth and environmental sciences and of interest to postgraduates who are not familiar with the application of multiavirate techniques and practising field biologists working in these areas.

Jim Fowler, De Montfort University, Scraptoft, Leicester, UK 

Lou Cohen, Loughborough University of Technology, Loughborough, UK

01. Introduction

 

02. Measurement and Sampling Concepts.

 

03. Processing Data.

 

04. Presenting Data.

 

05. Measuring the Average.

 

06. Measuring Variability.

 

07. Probability.

 

08. Probability Distributions as Models of Dispersion.

 

09. The Normal Distribution.

 

10. Data Transformation.

 

11. How Good are Our Estimates?

 

12. The Basis of Statistical Testing.

 

13. Analysing Frequencies.

 

14. Measuring Correlations.

 

15. Regression Analysis.

 

16. Comparing Averages.

 

17. Analysis of Variance - ANOVA.

 

18. Multivariate Analysis.

 

Appendices.

 

Bibliography and Further Reading.

 

Index.