Objectives of Programme's Learning Outcomes

• Describe, organise, analyse and prioritise the phenomena observed in the medical field
• Understand the expression of biological realities in absolute or relative terms, the orders of magnitude, proportions and probability
• Understand and apply mathematical translations of large models and biological phenomena through abstract reasoning
• Understand and use different graphical representations of numerical values and their relationships
• Develop reasoning skills
• Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, the rule of three, syllogism, analogy, Boolean logic, etc.)
• Understand and apply Bayesian inference
• Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
• Integrate reasoning in clinical approaches
• Manage doubt and uncertainty
• Manage their studies
• Read, interpret, and critique a scientific article
• Be a responsible practitioner
• Base reasoning on the data taken from scientific literature (evidence-based-medicine)

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