This module provides a foundation in statistical methods for students within the medical sciences to support evidence-based practice. It gives students the skills to produce new clinical research and to describe and draw inferences from biological, clinical and epidemiological data.

The statistical methods covered in the syllabus provide a foundation in producing, describing and drawing inferences from data and introduces the students to the use of the statistical software. These skills will be developed in more depth in other modules. The following is indicative of the content to be covered in this syllabus: • Study design (case-control, cohort, clinical trial), • Sampling methods (probability and non-probability), • Data types and descriptive statistics for biological, clinical and epidemiological data • Frequency tables and cross-tabulation, • Data manipulation, visualisation and analysis using open-source statistical software, • Discrete and continuous probability distributions, reference ranges for clinical laboratory data and statistical process control limits, • Point and interval estimation, measuring uncertainty and precision, • Hypothesis testing for means, proportions and variances with application to clinical case studies, • Evidence based practice in basic and applied research, • Theories and applications of bioinformatic techniques.

On successful completion of this module, students will be able to: • Distinguish between the design of observational and experimental studies in clinical research to be able to discuss the role of statistics in evidence-based medicine; • Describe biological, clinical and epidemiological data using summary measures and visualisation; • Quantify uncertainty in estimation including disease prevalence estimates; • Test hypotheses using statistical software; • Interpret the analyses of biological, clinical and epidemiological data.

On successful completion of this module, students will be able to: • Defend the choice of statistical methods for the analysis of biological, clinical and epidemiological data.

On successful completion of this module, students will be able to: N/A

The module will be taught using a mixture of lectures, small group work, analysis of datasets using open-source statistical software, and clinical case studies. It will use examples of published medical research to illustrate the application of methods and develop the graduate attribute of being curious (critical, knowledgeable and inquisitive). Students will develop hypotheses to be tested and be open-minded and independent (graduate attribute - agile) in the interpretation and use of evidence in medicine. The analysis of real-world datasets will provide opportunities for experiential learning.