This course provides students with experience in data analysis, data visualisation and experimental design techniques employed in the life sciences. This will utilise the statistical software package R, which is versatile and increasingly used in the life sciences. 

There is normally a 1-hour lecture and a 2-hour computer practical session on Wednesday. 

Statistics for the Life Sciences (Sem 1) 4Y option

The course will be assessed by a 90-minute examination (50%) and in-course assessment consisting of 2 components: a report (30%) and a class test (20%). 

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The aims of this course are to provide students with skills in handling and interpreting numerical data to understand and answer biological research questions. Students will also learn broadly how to critique the design and analytical approach in published research. These are key skills in the life sciences and transferable beyond. 

By the end of this course, students will be able to: 

■ Organise data in an appropriate form for statistical analysis;

■ Use R to visualise and analyse datasets, including the use of packages;

■ Perform a range of descriptive and inference statistical tests, and critically discuss their use and assumptions;

■ Explain and apply the process of choosing a particular statistical test and model selection;

■ Present visual and statistical results within a written scientific report;

■ Critique the experimental design and statistical approach of primary scientific literature in the life sciences;

■ Understand the mechanics and algebraic structure of General Linear Models;

■ Interpret and understand the output of statistical tests.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.