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.

Assessment Strategy

Threshold - (D- grade) A threshold student should have a basic knowledge of the scientific method, have a basic ability to quantitatively manipulate datasets using a range of fundamental mathematical tools using appropriate computer software, and be able to use and interpret statistical tests.

Good - (B grade) A good student should show competancy in quantitatively manipulating datasets using a range of fundamental mathematical tools, have a good ability to interpret datasets and be able to confidently use and interpret statistical tests using appropriate computer software.

Excellent - (A grade) An excellent student should have demonstrated a sophisticated knowledge of quantitatively manipulating datasets using a range of fundamental mathematical tools, have an advanced ability to interpret datasets and be highly skilled in the use and interpretation of statistical tests using appropriate computer software.

Learning Outcomes

• Apply inferential statistical tests to data to test a hypothesis, to investigate differences or relationships, interpreting the results in the context of the subject specific research question.

• Communicate the results of scientific investigations and data analysis in the style of a scientific report.

• Explain hypothetico-deductive reasoning, and formulate testable hypotheses.

• Identify and explain the most appropriate strategy for analysing data, including recognising the assumptions and limitations of each approach.

• Manipulate, summarise, visualise, and analyse data using Excel and R computer software.

• Numerically and graphically summarise data to communicate key scientific results.

• Plan and conduct a simple scientific investigation with appropriate regard to design and analysis issues.

• Produce simple maps to communicate scientific methods or findings.

• Report numerical values to an appropriate level of precision, and use appropriate units of measurement.