MEDICAL STATISTICS
Stampa
Enrollment year
2019/2020
2019/2020
Regulations
DM270
MED/01 (MEDICAL STATISTICS)
Department
DEPARTMENT OF CLINICAL-SURGICAL, DIAGNOSTIC AND PEDIATRIC SCIENCES
Course
MIDWIFERY
Curriculum
PERCORSO COMUNE
Year of study
Period
1st semester (01/10/2019 - 15/01/2020)
ECTS
2
Lesson hours
30 lesson hours
Language
Italian
Activity type
WRITTEN TEST
Teacher
VILLANI SIMONA (titolare) - 2 ECTS
Prerequisites
The course is part of the students' basic training together with Physics, preparatory to the lessons and activities in the nurses’ field. To better follow the course, the student must have basic knowledge of mathematics of scientific high schools’ program.
Learning outcomes
The course aims to provide the methodological principles for a scientific approach to the studies in clinical field. It is the first step in the knowledge that a nurse must have in order that the clinical research carried out is correctly set and evaluated.
In detail, the course aims to develop the theoretical and practical knowledge of the most frequent basic statistical methodologies (knowledge and comprehension), as well as the ability to correctly apply this knowledge both to new experimental situations and to published research studies (ability to apply knowledge and comprehension).
At the end of the course the student will be able to use the main study planning tools and basic statistical analysis on the data; to interpret the results of a statistical analysis in an awareness and critical way; communicate pertinently what emerged; to understand the published evidence and to critically assess what exists in relation to one's work context.
At the end of the course the student will be able to independently perform basic statistical analyses and communicate in an appropriate way the findings, as well as to understand and critically evaluate the published evidences in relation to their work context.
Course contents
Introduction to Statistic and research planning
Research Protocol.
- Population, sample and sampling methods (simple random sampling; stratified random sampling; cluster sampling). Introduction to sample size.
- Data organisation: database and dataset.
Tools for descriptive analysis and interpretation of data
- Description of statistical unit and type of variables. Frequency distribution for qualitative and quantitative variables. Graphics.
- Descriptive statistics: mean, median, mode, centiles, range variance, standard deviation, coefficient of variation.
- Pearson’s correlation coefficient.
- Introduction to probability. Probability axioms and conditional probability. Sensitivity and Specificity of a diagnostic test. False positive and negative. Positive and negative predictive values. Normal distribution probability.
Inferential statistics
- Test of hypothesis
- Parametric unpaired t-test.
- Chi-squared test.
Teaching methods
The plan of the course is based on academic lectures and practical sections (problem solving approach).