OverviewThe curriculum will review the approaches used by natural scientists in the design and analysis of research projects. The principles of experimental design and how these can be applied to field projects will be explained, together with the nature of both quantitative and qualitative data. An introduction to sampling strategies and the role of probability in inferential statistics will lead into the role of descriptive statistics and measures of variability in data exploration. This will be complemented by consideration of the application of both parametric and nonparametric statistics in data analysis (i.e. t-tests, ANOVA, regression, correlation and their nonparametric equivalents), coupled with training in the use of a statistical package to carry out such analyses. Finally, the rules underlying the appropriate presentation of statistical data in research reports will be discussed.
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Method of assessment
Assessment will be by means of two pieces of coursework that carry equal weighting (50% each). The first will be a written appraisal of a published research design and analysis: this will primarily test understanding of research design and statistical principles. The second will be a computer-based statistical analysis and write-up of statistical data: this will test the students' ability to carry out statistical tests on appropriate data. As much of the write-up will be graphical and statistical, there is not a specific word limit attached to these exercises.
Dytham, C. (2003). Choosing and Using Statistics: A Biologist's Guide. Blackwell: Oxford.
Hawkins, D. (2005). Biomeasurement. Oxford University Press.
The principles of experimental design and how they should be applied to field conservation projects.
The difference between quantitative and qualitative data and the research designs for which each is appropriate.
The use and application of descriptive and inferential statistics in quantitative data analysis.
The use and application of a range of parametric and nonparametric statistical tools in quantitative data analysis.
How to use appropriate statistical software to explore and analyse quantitative data.