ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 91 Hours of tutorials: 3 Expository Class: 36 Interactive Classroom: 20 Total: 150
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Center Faculty of Medicine and Dentistry
Call: First Semester
Teaching: Sin Docencia (En Extinción)
Enrolment: No Matriculable (Sólo Planes en Extinción)
The main objective of this subject is for students to become familiar with the basic concepts and techniques of Descriptive Statistics, Probability Theory and Statistical Inference. It is intended that students understand the need and usefulness of statistical methodology in research in Health Sciences, particularly in the field of Medicine.
The specific objectives of the subject are detailed below:
- To know how to discriminate between the objectives of a statistical analysis: descriptive or inferential.
- To know how to distinguish between a statistical population and a sample of it.
- To synthesize and describe a large amount of data, selecting the appropriate statistics for the type of variables and analyzing the relationships between them.
- To know the probabilistic basis of Statistical Inference, as well as the general principles of the most common probabilistic models.
- To know how to estimate unknown parameters of a population from a sample.
- To know the principles and applications of hypothesis testing.
- To know how to compare two populations based on their characteristic and unknown parameters.
- To know how to formulate real problems in statistical terms and apply Statistical Inference to their resolution.
- To be able to handle statistical software packages.
- To assume the need and usefulness of Statistics as a tool in their professional practice, being aware of the degree of subjectivity and the risk of decisions based on statistical results.
Topic 1.- Descriptive statistics.
Definition and objectives of Statistics. Statistics in medical research. Design of a study, population and sample. Types of statistical variables. Summary of the information contained in a sample: frequency tables and graphical representations. Measures of centralization, position, dispersion and shape.
Topic 2.- Probability calculus.
Random experiment. Event and sample space. Operations with events. Axiomatic definition of probability. Conditional probability. Independence of events. Product rule. Theorem of total probabilities. Bayes rule. Prevalence and incidence of a disease. Diagnostic tests: sensitivity, specificity and predictive values.
Topic 3.- Discrete random variables.
One-dimensional random variable concept. Discrete random variable. Probability mass, distribution function and survival function. Characteristic measures: expected value and variance. Binomial distribution. Poisson distribution.
Topic 4.- Continuous random variables.
Continuous random variable. Density function, distribution function and survival function. Characteristic measures: expected value and variance. The normal distribution. Cut-off points for binormal diagnostic tests. Central Limit Theorem. Approximation of the binomial distribution by the normal. Distributions associated with the normal: Chi-Square, T-Student.
Topic 5.- Pointwise and interval estimation.
Objectives of Statistical Inference. Parameter and statistical concepts. Samplig distributions of statistics. Pointwise estimation of the mean, variance, and proportion. Bias and variance. Confidence intervals for the mean (in normal populations) and for the proportion. Determination of sample size.
Topic 6.- Introduction to hypothesis testing.
Basic concepts: Null and alternative hypotheses; unilateral contrast and bilateral contrast; acceptance and rejection zones; type I error and significance level; type II error and power; p-value. Test on the mean (in normal populations) and on the proportion. Test of means comparison in normal populations (for two independent or paired samples) and of proportions.
Topic 7.- Tests for categorical variables.
Contingency tables. Observed frequencies and expected frequencies. Chi-square test of independence. Yates Correction. 2x2-contingency tables in the field of Medicine. Association measures: Relative risk and odds ratio.
Topic 8.- Simple linear regression model.
Scatterplot. Covariance and linear correlation coefficient. Least squares method. Inference on model parameters. Variability decomposition. The F-Test. Determination coefficient. Diagnosis of the model. Prediction.
- Alonso Pena, M., Bolón Rodríguez, D., Ameijeiras Alonso, J., Saavedra Nieves, A. and Saavedra Nieves, P. (2024). Manual de R para prácticas de Bioestadística. Servizo de Publicacións da Universidade de Santiago de Compostela. DOI: https://dx.doi.org/10.15304/9788419679536.
- Álvarez Cáceres, R. (2007) “Estadística Aplicada a las Ciencias de la Salud”. Editorial Diaz de Santos.
- Daniel, W.W. (2006) “Bioestadística. Base para el análisis de las ciencias de la salud”. (2ª ed). Editorial LIMUSA. Wiley.
- Douglas G. A. (1997) “Practical Statistics for Medical Research”. Ed. Chapman & Hall.
- Martín Andrés, A. and Luna del Castillo, J. (1994) “Bioestadística para las ciencias de la salud”. (4ª ed). Ediciones Norma.
- Martín Andrés, A. and Luna del Castillo, J. (1995) “50 +/- 10 horas de Bioestadística”. Ediciones Norma.
- Martínez González, M.A; Sánchez, A. and Faulin, J. (2006). “Bioestadística amigable”. 2ª ed. Editorial Diaz de Santos.
- Milton, J.S. (1994) “Estadística para biología y ciencias de la salud”. (2ª ed). Ed. Interamericana, McGraw-Hill.
- Paradis, E. (2003). R para principiantes. R Cran. Disponible en https:/ cran.r- project.org/doc/contrib/rdebuts_es.pdf
- Quesada, V. and others (1982) “Curso de ejercicios de estadística”. (2ª ed). Editorial Alambra.
- Rosner, B. (2000) “Fundamentals of Biostatistics”. (5ª ed). Wadsworth Publishing Company. Duxbury Press.
- Venables, W.N., Smith, D.M. and the R Core Team (2020). An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics (Version 3.6.3). Disponible en https:/ cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
- Verzani, J. (2005). Using R for Introductory Statistics. Chapman and Hall.
The general competences related to the subject of Biostatistics are:
CG28.- Obtain and use epidemiological data and assess trends and risks for decision-making on health.
GC31.- Know, critically assess and know how to use clinical and biomedical information sources to obtain, organize, interpret and communicate scientific and health information.
GC33.- Maintain and use the records with patient information for subsequent analysis, preserving the confidentiality of the data.
GC34.- Have, in professional activity, a critical, creative point of view, with constructive skepticism and research-oriented.
GC35.- Understand the importance and limitations of scientific thought in the study, prevention and management of diseases.
GC36.- Being able to formulate hypotheses, collect and critically assess information for problem solving, following the scientific method.
GG37.- Acquire basic training for research activity.
In the field of Biostatistics, the CG32 competence (related to the use of information and communication technologies in clinical, therapeutic, preventive and research activities) will also be worked on, despite not appearing on the subject sheet.
The specific competencies that students must acquire through the Biostatistics subject are listed below:
CEMII.32.- Know the basic concepts of Biostatistics and its application to medical sciences.
CEMII.33.- Be able to design and carry out simple statistical studies using computer programs and interpret the results.
CEMII.34.- Understand and interpret statistical data in the medical literature.
Competences CEMII.31 and CEMII.378 (related to the critical use of technologies and the management of computer applications, respectively) will also be worked on in the matter, even though they do not appear on its file.
In the Virtual Campus of the subject, students will find notes and problem sets. The theoretical and practical concepts of the contents are collected in multimedia presentations. The resolution of problems in the bulletins will also allow students to apply the contents of the subject. As for the follow-up material of the subject, in addition to the recommended bibliography, students will have complementary teaching material in the Virtual Campus.
As it is a subject of the old plan, interactive laboratory teaching with the R software will not be given. The contents corresponding to this part will be evaluated within the continuous assessment section with the considerations detailed in the following section.
Final exam (70%): the final exam will consist of several theoretical-practical questions and problems on the contents of the subject.
Continuous assessment (30%): As this is a subject from the old plan, the continuous assessment grade for the 2024-2025 academic year will be retained. If it is not passed/taken in the 2024-2025 academic year, an extra compensatory activity will be added to the final exam. In this case, students who wish to improve their continuous assessment grade would also be included. Therefore, if the extra activity is taken, it will determine the continuous assessment grade, discarding the one previously obtained.
The weight of the continuous assessment in the opportunity for recovery will be the same as in the ordinary call for the semester.
In cases of fraudulent completion of exercises or tests, the provisions of the “Regulations for the assessment of student academic performance and review of grades” will apply.
It is considered necessary to dedicate around 75 hours of individual work by the students. In addition, the students must practice solving problems based on the bulletins or the recommended bibliography.
To successfully pass the subject, it is also recommended that students practice solving problems from the bulletins.
The course material will be made available to students through the USC Virtual Campus. We intend for this platform to be the main way of communication with students, reinforced with MS Teams and email.
Paula Saavedra Nieves
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- paula.saavedra [at] usc.es
- Category
- Professor: University Lecturer
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicina-Aula 10 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 2 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 3 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 4 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 5 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 6 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 7 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 8 |
| 12.15.2025 12:00-14:30 | Grupo de examen | Medicine-Classroom 9 |
| 06.12.2026 09:30-12:00 | Grupo de examen | Medicine-Classroom 4 |
| 06.12.2026 09:30-12:00 | Grupo de examen | Medicine-Classroom 5 |
| 06.12.2026 09:30-12:00 | Grupo de examen | Medicine-Classroom 7 |
| 06.12.2026 09:30-12:00 | Grupo de examen | Medicine-Classroom 8 |