Module code: BMS2072

Module Overview

This module introduces students with a Biosciences background to a variety of research skills encompassing the principles of experimental design, ethics, data analysis and statistics. Students will be introduced to the statistical computing software R and Rstudio for performing their own data analysis. Throughout the module there will be a focus on approaches to acquiring, analysing and interpreting complex human data, bringing together elements of Biosciences, Engineering and Data Science. The module will also include elements of group activity, reflecting the reality of working with patients at the interface between biomedicine and technology.

Module provider

School of Biosciences and Medicine

Module Leader

BAILEY SG Dr (Biosc & Med)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

JACs code: L510

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative content includes:

Overview of key aspects of scientific study design and the types of studies performed on human subjects (clinical trials, epidemiology, genetic analysis, meta-analysis of data)
Medical and research ethics
Founding statistical principles
Data types and distributions
Hypothesis testing
Effect sizes
Experimental design:
Hypothesis setting
The role of controls
Powering your experiments
Critical evaluation of literature
The cost of science: how does it work in the real world?
Introduction to the statistical computing software R and Rstudio
Data visualisation in R
Data analysis in R using existing functions and packages 2017/18

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Ethics application 30
Coursework Data analysis and interpretation 30
Coursework Group critique and experimental design 40

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate

Their understanding of the procedures that regulate research involving patient or other groups and the process by which ethical approval can be gained.
Their ability to select the appropriate methods to visualise and analyse data utilising the statistical software R and Rstudio in order to draw scientifically robust conclusions from the data
Their understanding of experimental design procedures including hypothesis setting, use of appropriate controls and the principles of power calculations.
An ability to critically evaluate a reported experimental design

Thus, the summative assessment for this module consists of:

Ethical approval exercise (30%) – week 5 Students will be given a scenario for which they need to provide a narrative summary and the appropriate documentation required in order to seek ethical approval.
Data analysis and interpretation exercises (30%) – around week 12 Five exercises representing a variety of data types will be attempted including: statistical / other test selection, analysis of data by R, interpretation and presentation of key findings.
Group critique and experimental design (30% group work, 10% individual summary) – around week 10 15 min oral critique of a medical research publication by teams of 3-4 members. There should be a focus on the experimental design and any flaws or omissions, with modifications and improvements to be recommended. To include questions from “reviewers” (5 min). Each group member will also produce an individual 700 word executive summary containing key background information and their own justification for prioritisation of experimental improvements.

Formative assessment and Feedback

Ethical approval tutorials will involve small group discussions to aid students in formulating their application. 2017/18
Workshops where students will gain hands on experience of using R to visualise and analyse data in R will be timetabled; full support and feedback will be given during these sessions
Use of R using mock data will be formatively assessed in workshops to enable students to assess their understanding and ability then implement feedback
Combination of workshops and online applications through SurreyLearn will allow students to interactively assess their own understanding of probability and statistics
During the experimental design process additional small group tutorials and drop-in sessions will be timetabled for formative feedback.

Module aims

  • Introduce students to the main ethical considerations of working with patients, and the processes involved in governing such studies
  • Increase awareness of the different types of data that can be generated from human studies and the statistical and mathematical approaches to analysing such data
  • Introduce the founding concepts in experimental design, including sample size and power calculations
  • Introduce students to the statistical computing software R and Rstudio to visualise and analyse data using existing functions and packages
  • Provide an appreciation of health economics, from the cost of research to the value of using technology to improve patient management and outcomes

Learning outcomes

Attributes Developed
001 Demonstrate an understanding of the ethical considerations that apply to human studies, and relate these to the processes for regulating such work. KCPT
002 Design an experiment to meet a particular experimental objective KC
003 Show an understanding of basic statistical principles behind data generation and analysis KCPT
004 Demonstrate an ability to interpret data/results KCPT
005 Appreciate the limitations of presently available experimental methods and consider alternative approaches. KCT
006 Demonstrate an ability to use the statistical software R and Rstudio for visualising and analysing data KCPT
007 Critically evaluate publications against the criteria discussed within the module KCPT
008 Work with others to formulate an experimental plan, and present that plan to an audience of mixed backgrounds CPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

Methods of Teaching / Learning

The learning and teaching strategy is designed to: Introduce underpinning theories and concepts via lecture content, whilst relating these to practical skills and real-world problems / applications via a mixture of research seminars, problem based workshops and small group tutorial work.

The learning and teaching methods include

Lectures (approximately two per week), which may be online as pre-requisite material
Plus each week either:
Workshop (2 hours each – in some cases these will be virtual with protected time for completion of activites)
Or tutorial (1 hour) plus seminars (1 hour)

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Biomedicine with Data Science BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Biomedicine with Electronic Engineering BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2018/9 academic year.