Overview

This module provides students with the fundamental skills required by natural scientists to answer scientific questions with environmental data.

Theory is put into practice through computer sessions, to apply a range of data analysis techniques to environmental data. In the first semester students are introduced to the scientific method, how to describe samples numerically and graphically, and how to test hypotheses statistically to identify differences and relationships between variables.

In the second semester, as well as additional statistical theory and practical sessions, skills are applied in a subject specific project. In this project, students conduct a scientific investigation, including collecting and analysing data; the results of this data analysis are communicated in the style of a scientific report.

This module concentrates on providing students with the basic skills for natural scientists, which focus on measuring, mapping, and quantifying the environment. The course relies heavily on computer-based material and so students also learn how to use and evaluate on-line information, as well as how to converse, discuss and learn via the Blackboard virtual learning environment. The module begins by focusing on the scientific method, hypothesis setting and testing; these leading to the fundamental ideas concerning experimental design. Further topics include:

Introduction and description of distributions within scientific data

Ideas of probability

Unit systems, decimal places, orders of magnitude used in science

Data analysis and manipulation using Excel

Graphing of linear systems

Coping with non-linearity in nature (logs, etc.)

Examples of statistical tests: parametric versus non-parametric

Statistical tests using SPSS

Tests for difference: t-tests and ANOVA

Tests of association: regression and correlation

Assessment Strategy

Threshold (D- to D+)

A threshold student will demonstrate the ability to define and solve routine problems. They should have a basic knowledge of the scientific method, have a basic ability to quantitatively manipulate datasets using a range of fundamental mathematical tools, have a basic ability to apply and interpret spatial and temporal datasets and be able to use and interpret statistical tests.

Good (C- to B+)

A good student should be able to demonstrate the ability to define problems, and devise and evaluate solutions to both routine and unfamiliar problems. They will be competent at quantitatively manipulating datasets using a range of fundamental mathematical tools, have a good ability to apply and interpret spatial and temporal datasets and be able to confidently use and interpret statistical tests.

Excellent (A- and above)

An excellent student will be able to demonstrate the ability to define problems, devise and evaluate possible solutions, and to solve both routine and unfamiliar problems confidently. They will have a sophisticated knowledge of quantitatively manipulating datasets using a range of fundamental mathematical tools, have an advanced ability to apply and interpret spatial and temporal datasets and be highly skilled in the use and interpretation of statistical tests beyond the directly taught course.

Learning Outcomes

• Apply appropriate data analysis and statistical techniques to scientific data using Excel, R and SPSS computer packages and correctly interpret the outcomes

• Can choose appropriate units of measure and can compile basic numerical manipulation techniques, and dimensional analysis

• Can make use of mathematical and graphical techniques to describe scientific phenomena

• Develop an understanding of the scientific method to develop ideas, make observations, and set and test hypotheses

• Have an awareness of how to plan and conduct a simple experiment with appropriate regard to design and analysis issues