• code 11435
  • course 1
  • term Anual
  • type OB
  • credits 1

Main language of instruction: English

Other languages of instruction: Spanish

Teaching staff

Head instructor

Dr. Josep Maria HUGUET -

Office hours

The student should contact by email to arrange a meeting:

Dr Josep Maria Huguet (

Dr Adrian González (



In the event that the health authorities announce a new period of confinement due to the evolution of the health crisis caused by COVID-19, the teaching staff will promptly communicate how this may effect the teaching methodologies and activities as well as the assessment.

Biostatistics is a fundamental discipline underpinning Health Sciences. This knowledge is required in order to conduct research and critically appraise scientific literature. It validates research and is crucial to both its understanding and use.

Pre-course requirements

Basic knowledge of mathematics.


To know the basic statistical concepts and their applications in the Health Sciences.


To train students to perform basic statistical analysis using statistical software.


To train students in critical reading of scientific articles.



  • CB10 - Acquire learning skills that allow them to continue studying in a self-directed and autonomous mode.
  • CB6 - Knowledge and understanding to provide a basis or opportunity for originality in developing and / or applying ideas in a research context.
  • CB7 - To apply the acquired knowledge, and develop their ability to solve problems in new environments within broader (or multidisciplinary) contexts related to their field of study.
  • CB8 - Integrate knowledge and deals with the complexity of formulate judgments based on scientific evidence, from information that may be incomplete or limited, include reflecting on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CE1 - Ability to apply the scientific method, the experimental design and the biostatistics to solve a problem or corroborate hypothesis.
  • CE3 - Ability to apply bioinformatics tools used in basic research.
  • CG1 - Ability to integrate new knowledge through research and study, and deal with complexity
  • CG2 - Ability to review analysis and discussion of the experimental results and to issue the corresponding conclusions.
  • CT1 - Ability to work in multidisciplinary and multicultural groups

Learning outcomes

It is expected that students acquire the following learning outcomes:

  • Understand the basic concepts of descriptive statistics. Know how to apply these appropriately according to the type of variable.
  • Understand the concepts of probability and probability distribution
  • Understand the concept of hypothesis testing, random and systematic error and statistical significance.
  • Correctly use basic hypothesis tests.
  • Know how to interpret, both statistically and clinically, the results obtained in both descriptive and inferential statistics.
  • Acquire skills for database management and data analysis using statistical software.
  • Know how to present numerical results in the context of an article and / or research project.
  • Know how to critically read the statistical results presented in original articles and reviews within scientific literature.


  1. Introduction to Statistics. Definition. Concepts. Biostatistics and in the Health Sciences.
  2. Sampling. Observational vs. Experimental data. Theory of sampling.
  3. Descriptive Statistics. Definition. Frequency distributions. Graphical reporting. Measures of Statistics.
  4. Statistical Inference and the Normal distribution. Definition. Probability distributions. Properties of the Normal distribution. The Standard Normal distribution. Area under the curve. The Central Limit Theorem.
  5. Using Normal Distributions. Tests for Normality. Proportions. Mean. Confidence intervals. Sample size.
  6. Hypothesis testing. Definition. Concepts involved. Examples. Technique. Overview of tests. The Student’s t-distribution. Importance in hypothesis testing. Use of tables. Non-parametric tests.
  7. Comparing two populations (I). Introduction. Types of comparison tests. Equal variance. Student’s t-test. Welch’s Unequal variance t-test. Paired Student’s t-test. Proportions test.
  8. Comparing two populations (II). Mechanism of comparison. Sample size when comparing. The χ2 distribution. McNemar test.
  9. Relationship. Definition. Relationship between two continuous variables. Relationship between two dichotomous variables.
  10. Advanced techniques. Logistic regression, ANOVA and non-parametric statistics.

Teaching and learning activities

In person

Each two-hour session will consist of:

  • One hour of explanation of theoretical concepts.
  • One hour of practical application of the theoretical concepts.

Evaluation systems and criteria

In person

100% continuous assessment: online tests and practical exercises to be delivered on a weekly basis.


Bibliography and resources

  1. Altman DG. "Practical statistics for medical research". Londres: Chapman and Hall/CR. 1999.
  2. Martín Andrés A, Luna Del Castillo J. "Bioestadística para las Ciencias de la Salud". Madrid: Capitel Ediciones, S.L., 2004
  3. Milton JS. "Estadística para Biología y Ciencias de la Salud”, 3ª Ed. Madrid: Interamericana McGraw-Hill, 2001.
  4. Motulsky H. "Intuitive Biostatistics". N. York: Oxford University Press, 1995.
  6. Martínez-González MA, Sánchez-Villegas A, Faulín Fajardo FJ. Bioestadística amigable (2ª ED). Díaz de Santos. Madrid; 2006.
  7. Gordis L. Epidemiology. 3a edición. Ediciones Hancourt S.A., 2005.
  8. Szklo M, Nieto J. Epidemiología intermedia: Conceptos y Aplicaciones. Díaz de Santos; 2003.
  9. Argimon Pallás JM, Jiménez Villa J. Métodos de investigación clínica y epidemiológica. 3 ed. Elsevier España S.A.: Madrid; 2004.
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