Objectives of Programme's Learning Outcomes

• Understand, describe, analyse and prioritise biological phenomena
• Understand basic scientific knowledge and be able to use it
• Problem-solve
• Understand the expression of biological realities in absolute or relative terms, the orders of magnitude, proportions and probability
• Abstract, understand and apply mathematical translations of the main laws and biological phenomena
• Understand and use different graphical representations of numerical values and their relationships
• Control the molecular, morphological and functional approaches of normal and pathological conditions
• Understand experimental protocols in the biomedical domain
• Develop reasoning skills
• Understand the statistical and/or epidemiological methods
• Present a hypothesis and hypothetical-deductive reasoning
• Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
• Manage doubt and uncertainty
• Demonstrate developed interpersonal skills
• Use a rich vocabulary linking concepts and words accurately
• Adapt lexical and syntactic choices in communication register (everyday language, medical language or scientific terminology)
• Share knowledge and information
• Manage their studies
• Read, interpret, and critique a scientific article

Learning Outcomes of UE

Learning outcomes include :
- Understanding the most frequent tools used in descriptive statistics and how to use them practically;
- Interpreting graphical outputs such as box plots;
- Understanding basic principles of correlation (Pearson, Spearman) and linear regression;
- Being able to compute some basic probabilities (normal distribution and discrete distribitions);
- Understanding the basis of inferential statistics, which is a fundamental prerequisite before taking more advanced statistical courses.

UE Content: description and pedagogical relevance

Definition and aims of biostatistics. Variability in medicine. Types of variables. Univariate descriptive statistics (localisation and dispersion parameters) and graphical output. Bivariate descriptive statistics (linear correlation and simple linear regression). Frequency and probability of event. Elements of combinatorics. Conditional probability and Bayes theorem. Random variable (discrete and continuous). Probability density function. Central limit theorem. Binomial and Poisson laws. Normal and Z law. Use of statistical tables.

Prior Experience

Not applicable