Data Analysis for Conservation Biologists - DI508

Location Term Level Credits (ECTS) Current Convenor 2017-18 2018-19
Canterbury Spring
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6 15 (7.5)
Canterbury
(version 2)
Spring
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6 15 (7.5)

Pre-requisites

Only available to students registered for Biodiversity Conservation and Management, and Wildlife Conservation.

Restrictions

None

2017-18

Overview

This course is designed to introduce and re-affirm statistical concepts, and their correct use and relevance to field biologists. Introductory topics will include measures of central tendency, frequency distributions, the normal distribution, standard errors, and how sample parameters, and null hypotheses apply in real biological situations. Further topics will include one- and two-tailed tests, chi-squared test, regression analysis, and analysis of variance. The role of probability in field biology will be considered, and its application to biological questions. Throughout this taught course, emphasis will be placed on practical application of statistics as much as possible, and when and how they are applied. Since there is both a theoretical and practical component to this course, students should aim to link the theory presented in lectures with the practical sessions and field trip parts of the course. The field trips will be towards the end of the course, by which time students will have been exposed to sufficient statistical methods, and be ready to apply it. By the end of the module, students should have a knowledge of the underlying principles of biological statistics, be able to evaluate from a theoretical stand-point and in practise, statistical results, and have a sound appreciation of the benefits and limitations of different statistical techniques and their application to field biology.

The role of this module has been to provide students with the statistical knowledge to conduct their data analysis for their research project, and to reinforce the appreciation and knowledge of statistical methods within a biological framework. It is often the case that students in the second and third years of their degree are able to execute statistical analysis via computer programmes, but lack an appreciation of what the statistical results actually mean, and the ability to correctly interpret them in the context of their research. This module is designed to address these issues through a combination of lectures on statistical topics within a biological framework, and practical tasks and exercises.

Details

This module appears in:


Contact hours

12 Lectures, 12 Seminars and Field trips

Availability

Normally taken at Stage 2

This Module is available only to students on Biodiversity Conservation and Management, and Wildlife Conservation.

Method of assessment

The module is assessed as 100% coursework. Assessment is by (i) 80% coursework, and (ii) 20% module test, which is to be taken at the end of the course.

Preliminary reading

Recommended Text:
Fowler, J., Cohen, L. & Jarvis, P. (1998). Practical Statistics for Field Biology. John Wiley & Sons. Chichester, UK.

Ruxton, G. D. & Colegrave, N. (2003). Experimental Design for the Life Sciences. Oxford University Press.

Text for Supplementary Reading:
Ambrose, H. W. & Ambrose, K. P. (1977). A handbook of biological investigation. Hunter Textbooks, Inc.

Moroney, M. J. (1951). Facts From Figures. Penguin Publishers, London.
This book has been continuously in print since its publication: a useful and readable general introduction to the subject.
Rowntree, D. (1991). Statistics Without Tears. Penguin Publishers. This book explains how statistics works without defining complex calculations: useful for the ‘maths-phobic’.

Huff, D. (1991). How To Lie With Statistics. Penguin Publishers. A very readable, and classic, introduction on how statistics can deceive!

Clegg, F. (1990). Simple Statistics – A course book for the social sciences. Cambridge University Press. This book covers the necessary ground for social scientists without being too mathematical.

Cohen, S. S. (1988). Practical Statistics. London, Melbourne.

Thomas, D. H. (1976). Figuring anthropology: first principles of probability and statistics. Holt, Rinehart & Winston Publishers.

Forthofer, R. & Lee, E. S. (1995). Introduction to biostatistics: a guide to design, analysis and discovery. Academic Press, London.

Bancroft, H. (1970). Introduction to biostatistics. Harper & Row, Medical Division.

Henry, G. T. (1990). Practical sampling. Sage publications, Newbury Park, London.

Bishop, O. N. (1966). Statistics for biology: a practical guide for the experimental biologist. Longmans Publishers.

Slonim, M. J. (1960). Sampling: a quick reliable guide to practical statistics. Simon & Schuster Publishers.

Langley, R. (1968). Practical statistics for non-mathematical people. Pan Books Publishers.

Wardlaw, A. C. (2000). Practical statistics for experimental biologists. Wiley, Chichester.

Finney, D. J. (1980). Statistics for Biologists. Chapman & Hall Publishers.

Glantz, S. A. Primer of biostatistics. (1997). McGraw-Hill, Health Professions Division, London.

Sokal, R. & Rohlf, J. (1973). Introduction to biostatistics. W. H. Freeman Publishers, San Francisco.

Goldstein, A. (1964). Biostatistics: an introductory text. MacMillan Publishers, New York.

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

Students will acquire an appreciation of the different statistical tools that can be applied to different kinds of biological data, and a practical and theoretical understanding of the circumstances in which particular statistical tests must be applied to types of biological data.

By the end of the module, students should be able to know about, and discuss intelligently:

• The theoretical Normal Distribution, and its application to data analysis.
• Null Hypotheses, Type I and II Errors, Sample Strategies, and Independence.
• One- and Two-Tailed Tests, and Experimental Design.
• Analysis of Variance, and Chi-Squared.
• Bivariate Data, Regression Analysis and Correlation Coefficients.
• Practical Application of SPSS Statistical Software.

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