the course aims at providing the basic concepts of the descriptive and inferential statistics for a proper understanding and interpretation of biological data and results arising from their analysis.
Course Prerequisites
Basic knowledge of mathematics provided in upper secondary school.
Teaching Methods
Lectures including practice sections with examples from biomedical licterature. For the categories of students identified by Senato Accademico in the session of 23 March 2023, the inclusive methods, among those indicated, will be provided and established ad hoc for each student who should need them.
Assessment Methods
Written examination with practical exercises focused on the topics covered in the course, together with multiple-choice questions and/or short open-ended questions. Exam time: 90 minutes. It is allowed the use of the basic calculator and of the statistical tables (provided by the professor). For the categories of students identified by Senato Accademico in the session of 23 March 2023, the inclusive methods, among those indicated, will be provided and established ad hoc for each student who should need them.
Texts
MC Whitlock, D Schluter. ANALISI STATISTICA DEI DATI BIOLOGICI. Zanichelli Editore
P Verderio, CM Ciniselli, V Duroni. BIOMETRIA E LABORATORIO - Esercizi di statistica per biologi. PaviaUniversityPress
Contents
DESCRIPTIVE STATISTICS Introduction to statistics, descriptive and inferential statistics, population and sample, variables and types of data; frequency distributions (absolute, relative and cumulative) and data representation (tables and graphs); location and dispersion indexes.
PROBABILITY Probability theory and type of events; random variables, discrete and continuous probability distributions, statistical tables.
INFERENTIAL STATISTICS Sampling distribution of the mean, the central limit theorem, point and interval estimation; hypothesis testing, null and alternative hypothesis, type I and II errors; comparing two means: parametric and non-parametric tests (for paired and independent data); comparing more than two means: analysis of variance (ANOVA) and adjustment for multiple comparisons; contingency tables and association analysis; correlation and simple linear regression; introduction to experimental design.
