After completing the course, students should be able to: - Understand the fundamental concepts of biostatistics. - Demonstrate knowledge of study design principles and data analysis techniques in health sciences. - Summarize data and represent them graphically. - Comprehend probability theory and apply it to theoretical probability distributions. - Estimate population parameters - Conduct regression analysis and interprete the results. - Perform hypothesis testing with qualitative and quantitative variables and interprete the results. - Apply non-parametric statistics techniques.

K2: Understands and applies the most appropriate mathematical, statistical and computational tools within Neuroscience, appropriately using spreadsheets for data management, and appropriate graphical representations for data presentation. S1: Uses a variety of techniques to find, manage, integrate and critically evaluate available information for the development of professional activities in Neuroscience, especially in the digital sphere S5: Appropriately uses the scientific and technical vocabulary of the different subfields within Neuroscience. C2: Apply knowledge about the organisation, structure and function of the Central Nervous System (CNS) to contribute to the evolution and improvement of technologies and systems for computing, data handling and analysis. C3: Apply knowledge about technologies for the study of the Nervous System and the brain (Medical Imaging, brain-machine interfaces) to develop new systems for diagnosis and treatment, as well as and other applications within Neuroscience (Artificial Intelligence, Robotics) with the aims of improving the quality of life and furthering social progress. C4: Uses advanced mathematical, statistical and computational tools to increase and improve knowledge in neuroscience and its applications. C5: Apply your neuroscience knowledge in a unifying and integrated fashion as part of a multidisciplinary team (pharmaceutical sector, health industry, diagnostic techniques, health information technologies, government agencies and regulatory bodies. C6: Apply the results of your comprehensive training to your everyday professional activities, combining Neuroscience knowledge with a solid foundation of ethical responsibility and respect for fundamental rights, diversity and democratic values. C7: Apply the scientific and technical principles you acquired during your undergraduate training, together with your own natural learning capabilities, to better adapt to novel opportunities arising from scientific and technological development.

1. Introduction. Basic concepts in biostatistics. Role of statistics in the research phases. 1.1. Statistical Terms: Populations, Individuals, and Samples 1.2. Types of Variables. 2. Introduction to study design in Health Sciences. Types of studies in biomedicine. Data analysis techniques. 2.1. Principles of Study Design 2.2. Types of Studies in Biomedicine: Experimental Studies, Observational Studies, Systematic Reviews, and Meta-Analysis 2.3. Techniques for Data Analysis in Biomedical Studies 3. Descriptive statistics. Sampling methods. 3.1. Descriptive Statistics: 3.1.1. Measures of Central Tendency and Dispersion 3.1.2. Graphical Representation of Data. 3.2. Sampling Methods: Simple Random Sampling, Stratified Sampling, Cluster Sampling and Systematic Sampling 4. Probability. Theoretical probability distributions. 4.1. Fundamentals of Probability Theory 4.1.1. Random Experiment 4.1.2. Development of concepts related to sample space, events, and their properties 4.1.3. Definition of probability and properties 4.1.4. Conditional Probability 4.1.5. Law of Total Probability and Bayes' Theorem 4.2. Discrete Random Variable 4.2.1. Probability Function 4.2.2. Cumulative Distribution Function and its properties 4.2.3. Common discrete distributions: Bernoulli, Binomial, Poisson 4.3. Continuous Random Variable 4.3.1. Cumulative Distribution Function and its properties 4.3.2. Probability Density Function 4.3.3. Common continuous distributions: Normal, Uniform, Exponential 5. Estimation of population statistical parameters. Regression analysis. Multivariate analysis. 5.1. Estimation of Population Parameters 5.1.1. Point Estimation 5.1.2. Confidence Intervals 5.2. Regression Analysis 5.2.1. Correlation Coefficient 5.2.2. Simple Linear Regression 5.2.3. Multiple Linear Regression 5.3. Introduction to Multivariate Analysis 6. Statistical theory of hypothesis testing. Hypothesis testing with qualitative variables. Hypothesis testing with quantitative variables. 6.1. Basic Concepts of Hypothesis Testing 6.2. Hypothesis Tests with Qualitative Variables 6.3. Hypothesis Tests with Quantitative Variables 6.4. Hypothesis Tests for Comparing Two Populations 7. Non-parametric statistics. Bayesian statistics. 7.1. Introduction to Non-Parametric Tests 7.2. Common Non-Parametric Tests: Mann-Whitney, Wilcoxon 7.3. Bayesian Statistics: Principles of Bayesian Inference

Classroom lectures. Face-to-face classes: reduced. Student individual work. Final exam. Lectures supported by computer and audiovisual aids. Practical learning based on cases and problems, and exercise resolution. Individual and group or cooperative work. Individual tutoring for resolving doubts and inquiries about the subject. Group reinforcement tutoring when necessary.