Main objective of this course is to understand basic concepts of descriptive and inferential statistics. At the end of the course students are able to collect data and interpret them, and to make simple inference from sample to population. Moreover, a basic introduction to the free R statistical software will be provided.

Knowledge and understanding: elements of descriptive statistics, applications of probability; inferential statistics, tools necessary for a medical doctor.

Ability to apply knowledge and understanding: being able to read and apply elements of Statistics to experiments and research in the field of medicine.

Making judgements: being able to critically evaluate the results of experiments and scientific articles with the presence of data collection and analysis.

Communication skills: being able to express oneself appropriately on the basic topics of Statistics on biomedical-health applications, in particular in view of the degree thesis project.

Learning skills: being able to grasp the salient elements of new topics, in particular on research methodology and data processing in the biomedical field.

basic mathematics. These basic mathematics skills are addition, subtraction, multiplication, and division. Concepts included in basic math include logarithms, fractions, decimals, percentages, exponents, ratios, scientific notation, and formulas

Statistical methods in biomedical studies and for the design of randomized clinical trials and observational studies.

P.Armitage, G.Berry “Statistical Methods in Medical Research”, Third Edition, Blackwell Science. Teacher’s materials.

• What is statistics; which are the applications of statistics; who use it. • Role of statistics in the biomedical research area. • Basic notions of Surveys and Experiments. • Basic sampling concepts. • Type of variables (qualitative/quantitative). Scales of measurements. • Unitary and frequency distributions. • Numerical and graphical summaries of data. • Correlation and regression. • Basic probability. • Random variables, binomial distribution, normal distribution. • Basic concepts of hypothesis testing and confidence intervals.

Lectures for the theoretical part will be accompanied by a series of practical examples for each topic under study. Students active participation will be stimulated by means of practical examples of data analysis and statistical results interpretation.

Practical sessions in a computer classroom or with personal laptops will be organized to illustrate practical examples of data analysis using the free statistical software R.

Written exam via quiz carried out in the classroom by accessing the Moodle platform. The quizzes focus on the theoretical and practical topics covered during the course. The quiz consists of 16 questions, the student has 60 minutes to answer. Each correct answer is worth 2 points, to obtain the sufficiency (18/30) you need to answer 9 questions correctly. To get 30/30 you need to answer 15 questions correctly. To get 30 cum laude you need to answer all 16 questions correctly.

This course explores topics closely related to one or more goals of the United Nations 2030 Agenda for Sustainable Development (SDGs)