This module is designed to allow you to critically engage with the literature in order to design experiments and studies that produce meaningful data at a level that would be of publishable quality for a Bioscientist. This module will provide the theoretical basis of the principles of experimental design in conjunction with the statistical framework within which to apply the fundamental concepts

A main core of the module will be the acquisition of methods and techniques designed to allow the development of methodologies to collect both quantitative and qualitative experimentally derived data. Additionally the module will also be dedicated to developing the essential management and leadership tools needed to implement and drive a successful experimental research project.

A range of topics will be covered and include areas such as scientific questioning, hypothesis framing, hypothesis testing, data management, questionnaire design, semi-structured interviews, methods of data reduction and exploration, regression analysis, multivariate techniques and advanced tests for difference.

Exercises and tuition will help the explorations and testing of the data handling and statistical analysis capabilities of software such as Excel, SPSS or R.

1. Data presentation and analysis portfolio 2000 Words (30%) [Learning Outcome 2]

2. Project methodology, design and plan, approx. (3000 words) accompanied by oral presentation (20 minutes) to communicate project rationale and experimental design to peers. (30% written, 30% oral and 10% peer assessment) [Learning Outcomes 1, 3 4]

IT laboratories equipped with SPSS and R.

Dytham, C (2011), Choosing and Using Statistics: A biologist’s guide (3rd Edn), Wiley-Blackwell, Chichester, UK
Kabacoff, R I (2011), R in Action: Data analysis and graphics with R, Manning Publications Co, NY, USA
Pallant, J (2013), SPSS Survival manual: A step by step guide to data analysis using IMB SPSS (5th edn), McGraw-Hill Education, Berkshire, UK
Silverman, D (2015) Interpreting Qualitative Data 5th Edition. Sage Publications Ltd. NY
Wu, CFJ and Hamada, M. (2009) Experiments: Planning, Analysis, and Optimization. Wiley-Interscience, NY

1. Demonstrate professional level research planning skills appropriate to a professional Bioscientist. (Analysis, Enquiry, Problem Solving, Application).
2. Display advanced skills in data handling, analysis and the use of standard statistical software. (Learning, Analysis, Problem Solving, Application).
3. Effectively communicate the salient features of your experimental design and planning to a professional audience. (Communication).
4. Produce a critical and evaluative peer review appropriate to a professional publication. (Reflection, Knowledge and Understanding).

This module will contain tutor directed workshops designed to elucidate, explain and help understand the indicative content. This will normally involve 2 hours sessions, weekly.

Computer-based statistical exercises will supplement the directly taught component. This will be designed to be studied independently and used to develop specific ideas introduced in the workshop sessions.

Directed independent problem-solving/tasks, to include distributed learning material, guided reading and completion of assessment tasks will occupy the remainder of the time.