MEDICAL STATISTICS
cod. 03848

Academic year 2024/25
2° year of course - First semester
Professor
Sara TAGLIAFERRI
Academic discipline
Statistica medica (MED/01)
Field
Scienze propedeutiche
Type of training activity
Basic
10 hours
of face-to-face activities
1 credits
hub: -
course unit
in

Integrated course unit module: RESEARCH METHODOLOGY

Learning objectives

After the course students should be able:
- to understand the nature of a variable by assessing its distribution;
- to manage a simple database;
- to critically read a basic biomedical scientific article, understanding its structure.

Prerequisites

Knowledge of basic mathematics, basic knowledge of English.

Course unit content

Introduction to medical statistics. The types of studies in reserach and clinical practice. Descriptive statistics: qualitative and quantitative variables. Measures of central tendency, dispersion, symmetry for quantitative variables. The normal and non-normal distribution. Skewness and Kurtosis. Kolmogorov-Smirnov’s e Shapiro-Wilk’s tests. Elements about binomial distributions. Parametric and Non-Parametric Inferential statistics. The sampling theory and central limit theorem. Hypothesis test on a mean. Constraints and degrees of freedom. T-Student distribution. Repeated and independent measures t-student test. Non-parametric test. The rank. Mann-Whitney’s and Wilkoxon’s tests. The frequency for categorical/qualitative variables. The 2*2 contingency tables. Chi-square test for independent data. Correlation coefficient R. Pearson’s and Spearman’s tests. Simple linear regression.

Full programme

To see Contents.

Bibliography

Classroom slides will be uploaded on Elly after 2/3 of the course. Some reference books:
- WW Daniel and CL Cross - Biostatistica, concetti di base per l’analisi statistica delle scienze dell’area medico-sanitaria - Ed. EdiSES;
- SA Glantz - Statistica per discipline biomediche - McGraw-Hill.

Teaching methods

During the classroom lectures the topics of the module program will be illustrated and commented. Each theory argument with mathematical formulae will be explained through simple and practical examples, to allow students focusing on basic concepts more than the formulae themselves. Furthermore, the use of opensource software in the information processing module will permit to the student to repeat the statistical tests treated during classes for a better comprehension of their use and meaning. Several technical terms will be used both in Italian and in English, to develop student’s independence in reading international scientific literature, especially in the field of biomedical laboratory.
Lectures will be held on-site in compliance with safety standards, provided that further instructions on the ongoing health emergency are not implemented. Supporting material will be available on the specific, student-reserved platform (Elly) and will include slide presentations.

Assessment methods and criteria

The verification of the achievement of module objectives is a written test, mainly consisting of questions about the arguments treated during the lessons. It will include multiple choice questions and exercises. Student’s knowledge and understanding of both theory and practice principles and their application in medical and biological fields will be evaluated. The grade will be in 30/30 and its weight will be based on the credits of this module respect to those of the integrate course.

Other information

None.

2030 agenda goals for sustainable development

- - -

Contacts

Toll-free number

800 904 084

Student registry office

E. segreteria.medicina@unipr.it
T. +39 0521 033700

Quality assurance office

Education manager:
Sandra Cavalca

T. +39 0521 034908
E.  didattica.dimec@unipr.it
E.  sandra.cavalca@unipr.it 

President of the degree course

Prof. Tullio GHI
E. tullio.ghi@unipr.it 

Faculty advisor

Dott.ssa Serena Neri
E. serena.neri@unipr.it

Career guidance delegate  

Dott.ssa Serena Neri
E. serena.neri@unipr.it

Director of Professionalising Teaching Activities (DADP)

Dott.ssa Serena Neri
E. serena.neri@unipr.it

Erasmus delegates

Prof.ssa Thelma Pertinhez
E. thelma.pertinhez@unipr
 

Quality assurance manager

Prof.ssa Paola Affanni
E. paola.affanni@unipr.it

Tutor

Dott.ssa Francesca Frati
E. francesca.frati@unipr.it 

Dott.ssa Martina Dardari
E. martina.dardari@unipr.it