乱伦秀

鈥淏IOL0001 Quantitative Biology鈥� is designed to develop and/or enhance your skills in experimental design and data handling in a context appropriate for biological sciences. The module will cover data collection and processing and will be illustrated with a range of real world biological examples and data sets from work in the department of Genetics, Evolution and Environment.

In addition to lectures, you will attend computer workshops and problem-based tutorials. In the workshops, you will learn how to use the software researchers use and have the opportunity to apply the statistical techniques that you have covered in the lectures. You will learn how to interpret and present these findings in a professional format.

By the end of this module, you will have a grasp on statistics which will allow you to interpret many of the findings reported in research papers. You will be able to apply these statistical skills in your own research right from the start of your undergraduate career (for example, the BIOL0055 Field Course or the BIOL0005 Research Project) and through to its end (for example, your 3rd/4th year research project)

A-level Maths is not a pre-requisite. For students who have already completed A-level Maths or related subjects this course will provide you with the opportunity to apply your knowledge in a specifically biological framework and enable you to develop your skills to a greater depth. You will also begin to develop your science communication skills, specifically writing.

This module is compulsory for first year Biological Sciences students and can be taken as an elective by students (including affiliate students) on other degree programmes.

Indicative topics 鈥� based on module content in 2022/23

• Why we use statistics
• Samples and populations
• Confidence intervals of a proportion
• Introduction to hypothesis testing and the Chi squared tests
• Variables and visualising data
• Guassian distribution
• Confidence interval of a mean
• Comparing two samples with the t-test
• Computer modelling
• One way Analysis of variance
• Comparing sample variances with the F-test
• Correlation and regression
• Experimental design
• Non-parametric statistics
• Using Excel and R to analyse data sets

Module Aims

• To introduce students in the biological sciences to basic statistical theory and analysis
• To demonstrate that a fundamental understanding of statistical analysis is vital in all fields of research in the biological sciences
• To introduce students to reading the scientific literature and develop their skills in scientific writing
• Develop an introductory understanding of how to use R to analyse basic biological data sets

Learning Objectives

By the end of the module, students should be able to:

• Have an enhanced knowledge of statistical analysis and appreciation of its role in the Biological research
• Apply the statistical methods taught on the course to analyse data sets from real experiments and draw conclusions about the underlying biological processes
• Report, communicate and discuss statistical methods and results in the style of a scientific paper
• Perform basic data handling, manipulation, presentation, and analysis in R