66756 - Experimental Design and Data Analysis

Learning outcomes

At the end of this course the student knows the principal concepts of experimental design to plan and to analyse results from field and laboratory experiments. The student should be able to discriminate natural variability and anthropogenic impacts on different marine populations and communities. Moreover he/she should able to manage data matrices by using uni- and multivariate statistical techniques.

Course contents

Experimental design: introduction to the scientific method; Sampling designs and methods; Components of the experimental design; Introduction to the analysis of variance as a method for a correct planning of the experimental design; Analysis of variance: one-way designs; hierarchical designs, factorial designs; designs to assess the environmental impact. Introduction to regression and correlation; Introduction to multivariate analyses; Multivariate data; distance, dissimilarity and similarity measures; Introduction to classification and ordination techniques; Statistical tests on multivariate data; Analysis of spatial and temporal patterns; Relations between biotic data matrices and environmental variables. Use of software packages for graphical analysis and statistical tests.

Prerequisites

Knowledge of basic statistics tools, with particular reference to probability and statistical inference theories. These topics are covered in the Module 3 of the course FUNDAMENTALS OF SEA SCIENCES

Readings/Bibliography

• Copy of Power point lessons
• Experimental Design and Data Analysis for Biologists - Gerry P. Quinn and Michael J. Keough - Cambridge University Press
• Statistica di base - Lamberto Soliani. Ed PICCIN
• An Introduction to R - W. N. Venables, D. M. Smith and the R Core Team, https://cran.r-project.org/manuals.html

Teaching methods

The course consists of 6 credits, of which 4 credits of lectures and 2 credits, corresponding to 24 hours, of practical exercises in the computer lab.

The practical exercises are carried out using both the students' personal computers and the computers made available by the University. The aims of the practical lessons are to analyse data according to different theoretical approaches and to become familiar with software suitable for the analysis of different types of data and experimental designs. A specific module of 1CFU will be dedicated to the use of "R", a free software environment for statistical calculation and graphical analysis.

Both during the lectures and during the data analysis exercises the student-teacher interaction and the discussion between students is actively favoured.

All activities will be held in person according to pandemic and health situations.

Assessment methods

Assessment of learning is based on the final examination, which evaluates the acquisition of the expected knowledge and skills through two practical tests, each lasting two hours.

The examination is designed to assess the achievement of the following learning objectives:

• Knowledge of the statistical methods presented during the lectures.
• Ability to apply these methods to the analysis of specific sampling designs.
• Ability to analyze multivariate data matrices, including both biotic and abiotic data.
• Practical ability to use specialized software packages to perform statistical analyses and produce graphical outputs.

The two examination tests will be carried out using two different software programs. R will be used for univariate and bivariate analyses, while PRIMER will be used for multivariate analyses. Each test will contribute 50% of the final grade.

With regard to the assessment of learning, the use of artificial intelligence (AI) is prohibited. Any use of AI constitutes a violation of academic integrity.

The examination dates will be published on the AlmaEsami website (https://almaesami.unibo.it ). Students must register for the examinations exclusively through the procedures provided on the AlmaEsami website.

Students with learning disorders and/or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Teaching tools

Power point lessons; Practical exercises in the computer lab on real case studies in different marine habitats. The teaching material will be available in the dedicated virtual space (https://virtuale.unibo.it/).

 

Office hours

See the website of Marina Antonia Colangelo

See the website of Massimo Ponti