Integrated Master in Pharmaceutical Sciences




Learning outcomes

The students should consolidate or acquire the fundamental concepts of Probability and learn the basic methods of parametric and nonparametric Statistical Inference, which are an essential tool to the decision in situations of uncertainty. Thus, the students should be able to identify and to carry out the appropriate statistical methods to analyse the data in a given situation.
These skills are of great importance in the field of pharmaceutical sciences, both from an applied perspective as well as in the context of scientific research.


Population and sample. Exploratory data analysis. Sampling characteristics and graphical display of data. Probability: concepts and properties. Conditional probability and independent events; Bayes theorem. Discrete and continuous random variables; population parameters. Detailed study of some important probabilistic models. Sampling distributions of empirical moments in normal populations. The Central Limit theorem. Parametric Statistical Inference: point estimators; confidence intervals for the proportion and for the parameters of a normal population; hypothesis testing. Tests for the proportion and for the parameters of a normal population; large-sample tests. Statistical inference about the difference between two proportions, the difference between the means of two populations and the ratio of variances for normal populations. Analysis of variance. Nonparametric Statistical Inference: nonparametric statistical methods to study one population and to compare two or more populations.