NCHNAP491 Maths, Statistics, Experimental Protocol and Analysis Course Descriptor

Course Summary

This course examines the mathematical and statistical principles that are an essential component of the professional laboratory/technician scientist’s knowledge and skills, such as units, dimensions, exponential logarithms, statistical techniques, probability distributions, sampling, significance testing and confidence limits, regression and correlation. Learners will apply their mathematical knowledge to scientific data. The course then examines the scientific method and experimental protocol, i.e. experimental design and procedure, reproducibility of results, mathematically and statistically robust analysis, clear reporting and hypothesis testing.

Course Aims

• To train learners to understand and apply principles, theory and techniques that underpin experimental protocol.
• To employ rigorous and exacting methodologies to record and detail scientific experiments and research.
• For learners to have a solid grounding in essential mathematical and statistical techniques.

Learning Outcomes

On successful completion of the course, learners will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year. 

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• Online learning
• Online discussion groups
• Online assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
• One-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
• Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Assessment will be in two forms:

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi or tri-partite reviews will monitor and evaluate the learner’s progress.  

Feedback is provided on summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books 

• Robeva, R., & Hodge, Terrell. (2013). Mathematical Concepts and Methods in Modern Biology (1st edition).
• Clarke, G. M. (1994). Statistics and experimental design : an introduction for biologists and biochemists (3rd ed.). London : E. Arnold ; New York : Halsted Press.
• Roff, D. A. (2006). Introduction to computer-intensive methods of data analysis in biology. Cambridge, UK ; New York : Cambridge University Press.

Journals

Learners are encouraged to read material from relevant journals on maths, statistics, experimental protocol and analysis as directed by their course leader.

Electronic Resources

Learners are encouraged to consult relevant websites on maths, statistics, experimental protocol and analysis.

Indicative Topics

• Statistics
• Experimental Protocol
• Data Analysis