Course Topics
The course, structured into three parts, will cover the following topics: Part One: DESIGNING A STATISTICAL SURVEY - Nature and origin of sample surveys. - The main stages of a sample survey. - Key measurement tools in social research: interviews and questionnaires. - Questionnaire design and administration techniques - Sampling methods. Part Two: DATA ANALYSIS - Probability calculation: introductory concepts. - Binomial and Normal distributions. - Statistical inference: confidence intervals. - Statistical inference: hypothesis testing. Part Three: PRACTICAL EXERCISES Fifteen hours of practical exercises are planned to complement the data analysis. During these sessions, students will learn how to analyse data and indicators from official sources using the SPSS statistical software package.

Teaching format
The course consists of 45 hours of classroom teaching, during which the lecturer will present various topics. Several of these hours will be dedicated to practical exercises relating to data analysis. The lessons will be presented using PowerPoint, and the presentations will be made available to students on Teams. The teaching approach is mixed, combining theoretical lectures with practical exercises and interactive activities, with greater student involvement during the practical sessions.

Educational objectives
The course aims to provide students with a solid mastery of the main methodological and inferential contents of statistics necessary for the quantitative study of social phenomena. In particular, the teaching intends to develop analytical skills useful for understanding, designing, and conducting statistical surveys, as well as correctly applying statistical analysis tools and techniques in the reading and interpretation of socio-economic data. Students will be able to identify, select, and appropriately use the most suitable statistical tools for describing individual phenomena or analyzing relationships among multiple variables. Moreover, they will develop the ability to critically evaluate the results of the analyses conducted, also considering the quality and reliability of the sources used. Knowledge and understanding: By the end of the course, students will have acquired familiarity with the fundamental concepts of inferential statistics. They will be able to understand the logic underlying the generalization of results from a sample to a population, recognizing the conditions for valid inference and the possible presence of errors. Specifically, they will be able to: - Understand the principles of probability and their applications in inferential statistics; - Understand the meaning and usefulness of confidence intervals and hypothesis testing for comparisons between groups and variables. Ability to apply knowledge and understanding: Students will be able to apply the methodologies learned to real socio-economic data, drawn from official sources or field surveys. In particular, they will be able to: - Design a simple statistical survey, with particular attention to defining objectives, variables, and data collection techniques; - Use software tools for data management, processing, and representation; - Apply inferential statistical techniques to test hypotheses and draw conclusions based on empirical evidence. Autonomy of judgment: Students will develop solid autonomy of judgment in interpreting data and choosing the most appropriate statistical techniques depending on the type of phenomenon analyzed and the research objectives. They will be able to critically assess the reliability of the results obtained, recognize methodological limitations of the analyses performed, and formulate well-founded considerations based on empirical evidence. Particular emphasis will be placed on the ability to discern between alternative approaches and to select the most appropriate solution for the given context. Communication skills: Students will acquire appropriate technical language and will be able to effectively communicate the results of statistical analyses both in written and oral form. They will be able to present tables, graphs, and numerical summaries clearly and coherently, adapting the communicative style to the context. Learning ability: The course will provide students with the necessary foundation to autonomously continue their learning path in statistics. They will be capable of exploring more complex topics, consulting advanced texts, and using digital tools for data analysis.

Assessment
The course exam consists of two parts: - Theoretical: Assessed through a written test with questions and exercises aimed at evaluating the student's knowledge and understanding of the course topics, as well as their ability to apply these skills to practical cases. In addition, a data anaysis test using SPSS software is also required. - Practical: Assessed through a short paper in which the student must design a questionnaire and provide a brief description of the hypothetical statistical survey.

Evaluation criteria
The final grade will be the weighted average of the written test (5/6) and the practical test (1/6). Both parts must be passed to pass the final exam. Additionally, two midterm tests will be administered during the course (not mandatory). Successfully passing both midterms (one of which will include the practical component) will count as the final exam grade, calculated as the weighted average of the two tests, following the same criteria outlined above. - Evaluation criteria for the written test include: accuracy of the answers, appropriateness of personal comments in the relevant sections, and knowledge of the methods needed to solve the proposed exercises. - Evaluation criteria for the practical test include: knowledge of the software and techniques required to solve the assigned task, and the adequacy of the interpretation and commentary of the results obtained.