The content of the Biostatistics course aims to give students a general introduction to the basic concepts of statistics, so they can understand statistical reasoning and correctly apply these tools to the design and analysis of experiments.

Lectures: Students acquire the core scientific knowledge of the course by attending lectures, which they complement with personal study of the topics covered. Classes will be dynamic and interactive, incorporating active-participation tools to encourage understanding and reflection on the concepts presented.

Problem-solving seminars: The knowledge acquired in lectures is applied to solving practical cases. These sessions are devoted to problem-solving, discussion of concepts and applications, and to student presentations, fostering active participation, critical thinking, and scientific communication skills.

Computer lab sessions: This part of the course is essential for consolidating the knowledge acquired. Students will learn to use the R language and various statistical packages to implement the analyses seen in the lectures. Basic data visualization techniques will also be covered, to communicate results clearly and effectively.

In addition, as a new feature for the 2026/27 academic year, the course will take part in the AIR-LAB teaching innovation project, working with real air quality data collected on campus. This data, generated and analyzed by the students themselves, will allow them to apply their knowledge of biostatistics and data visualization in a real-world context. In this first year the activity is set up as a non-graded pilot: students, working in groups, will analyze this data and present their results at the end of the course.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

The competencies of this course will be assessed through continuous assessment, which includes written exams, practical tests, and individual assignments.

The assessment system is organized into three modules, each carrying a specific weight in the final grade.

Theory

Assessment will be carried out through two midterm exams. The first midterm will carry a weight of 20% and the second midterm a weight of 30%. The final resit exam is aimed at students who have not passed one of the midterms and will carry the same weight in the final grade as the corresponding midterm.

Problem-solving seminars and oral presentation

Assessment of the problem-solving seminars will be carried out through short in-class exercises. Students must solve and discuss the problems in front of their classmates. This component accounts for 10% of the final course grade.

There will also be a group oral presentation in which students present the statistical analysis of a dataset or case study. This activity will carry a weight of 20% of the final course grade and will be assessed through a combined system of self-assessment, peer assessment, and instructor assessment.

Labs

Assessment of this component will be carried out through a practical test in the computer lab. In this session, students must use the appropriate statistical software, enter the data from a study, propose an analysis, and answer specific questions. This test will carry a weight of 20% of the final grade.

Additional considerations

Students must notify the instructors by email, during the first day of class, whether they choose continuous assessment or single assessment.

The percentages corresponding to seminars and labs will apply provided the student has obtained a minimum grade of 4 in each theory midterm, or in the corresponding resit exam. A grade of 4 does not mean the student has passed theory outright, which requires a grade of 5 or higher, but it does allow that grade to be averaged with the seminar and lab grades.

The final course grade will be calculated by weighting the grades of each component (theory, seminars, and labs). However, a grade of 4 or higher in the first and second midterms, or in the resit exam, will be required in order to calculate the final grade.

Students who have passed the course through the midterm exams and wish to improve their grade may sit the theory resit exam. Sitting this exam means forfeiting the grade previously obtained in the corresponding midterm.

Students who, having chosen the continuous assessment track, have not taken part in the continuous assessment activities or submitted the problem-set assignments, may be assessed through the final exam. In this case, the final grade cannot exceed 70% of the maximum grade. This 70% cap applies exclusively to students on the continuous assessment track; it does not affect students who chose the single assessment option, whose exam can reach 100% of the final grade.

A final grade of 5 or higher is required to pass the course.

Repeating students must complete all assessment activities, including the problem-set submissions, participation in the seminar presentation, and the practical assessment.

Attendance at lab sessions is mandatory.

To be eligible for the resit, students must have already been assessed on activities whose combined weight amounts to at least two-thirds of the total course grade. Accordingly, students will receive a grade of "Not assessed" when the activities completed account for less than 67% of the final grade.

Single assessment

The single assessment option consists of a single exam covering theory (70%), computer labs (15%), and problem sets (15%). This exam will account for 100% of the final grade.

The single-assessment exam will coincide with the date of the second midterm of the continuous assessment track, while the practical component will coincide with the date of the continuous assessment's practical exam. The resit for the single assessment option will coincide with the resit for the continuous assessment track.

Use of AI

In this course, the use of Artificial Intelligence (AI) technologies is permitted exclusively for support tasks, such as literature or information searches, R code review, text correction, or translations. Students must clearly identify which parts were generated using these technologies, specify the tools used, and include a critical reflection on how they influenced the process and the final outcome of the activity. Lack of transparency in the use of AI will be considered a breach of academic integrity and may result in a partial or full penalty on the activity, as well as additional sanctions in more serious cases.

Fraud protocol

Any irregularity committed during an assessment activity (academic fraud, plagiarism, or improper use of AI, except in authorized cases) that could significantly alter the grade will result in that activity being graded 0. If the course syllabus establishes a minimum grade on this activity as a requirement to pass the course, or if multiple irregularities occur within the same course, the final grade will be 0. In addition, disciplinary proceedings may be initiated against the student responsible for any of these irregularities.