The University of Southampton
Courses

# BIOL2008 Quantitative Methods in Biological and Environmental Science

## Module Overview

This module develops analytical skills required for the final year Honours Project, scientific research in general, and your future career. The major skills are computer literacy and graphical presentation, understanding of scientific method and hypothesis testing, a few simple mathematical concepts, and basic methods of statistical analysis, including non-parametric tests, analysis of variance and data modelling.

### Aims and Objectives

#### Module Aims

The aim of this module is to develop analytical skills required for the final year Honours Project, scientific research in general, and your future career. The major skills are computer literacy and graphical presentation, understanding of scientific method and hypothesis testing, a few simple mathematical concepts, and basic methods of statistical analysis, including non-parametric tests, analysis of variance and data modelling.

#### Learning Outcomes

##### Learning Outcomes

Having successfully completed this module you will be able to:

• Enter data on a spreadsheet, check for errors, use spreadsheet formulae, and plot, label and annotate spreadsheet graphs.
• Draw conclusions from the outputs of statistical analyses
• Calculate logarithms and other simple transformations and the equation of a straight line.
• Outline the nature of scientific knowledge and the processes of framing and testing hypotheses.
• Explain the concepts of the null hypothesis, p-value, statistical power, type I & II errors, significance and non-significance, and draw appropriate conclusions for the hypotheses being tested.
• Calculate the mean, median, mode, standard deviation, standard error, confidence intervals and inter-quartile ranges of a sample.
• Explain the difference between parametric and non-parametric statistics, and the assumptions that underlie them.
• Choose the appropriate statistical test for a variety of simple problems, and plan and design experiments before collecting data, taking account of the statistical techniques you will use.
• Use a statistical model to make a statement about predicted relationships.
• Perform simple non-parametric and parametric statistical tests of dependency, variance and correlation, using an appropriate software statistics package.

### Syllabus

Scientific method and theories, hypothesis testing, biological data, simple data handling and spreadsheets, graphics and report writing, simple statistical tests, non-parametric tests, contingency tests, multivariate methods, one-way and two-way analysis of variance, statistical modelling, regression and correlation, how to plan an analysis and choose an appropriate test.

#### Special Features

For features such as field trips, information should be included as to how students with special needs will be enabled to benefit from this or an equivalent experience. Lectures and practicals will develop observational skills and guide you towards authority in statistical analysis and critical interpretation of data through practise with concepts.

### Learning and Teaching

#### Teaching and learning methods

Teaching and learning will involve lectures, measurement of biological samples, computer-based practicals.

TypeHours
Independent Study107
Lecture23
Practical classes and workshops20
Total study time150

#### Resources & Reading list

Agresti, A. & Franklin, C. (2013). Statistics: The Art and Science of Learning from Data.

### Assessment

#### Summative

MethodPercentage contribution
Class practicals 8%
Class practicals 4%
Class practicals 16%
Class practicals 8%
Class practicals 4%
Examination 60%

#### Referral

MethodPercentage contribution
Coursework 40%
Examination 60%

### Linked modules

Pre-requisite: Core Skills in the Life Sciences 2016-17

#### Pre-requisites

To study this module, you will need to have studied the following module(s):

CodeModule
BIOL1020Core Skills in the Life Sciences
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