Learning objectives
Knowledge and understading
The course aims at providing knowledge and comprehension skills of statistical tests and analyses. The knowledge not only of basic statistics but also of statistical analyses typical of biotechnology will give students the possibility to comprehend when and how to apply such tests and analyses as well as to interpret appropriately studies and papers including statistical applications.
Applying knowledge and understading
The skills provided within the course will enable students to organize concretely experimental data and to choose the appropriate statistical test. Such a capacity will make students able to use applied statistical packages. Students will be able to use their learned skills to write popular and scientific reports, supporting their biological, medical, or genetic hypotheses with appropriate statistical methodologies and providing a correct interpretation of the results.
Making judgements
The skills students will learn during the course will allow them to form an informed opinion on the potentialities and strengths of the various statistical tests, on the effects of possible wrong choices and evaluations as well as on the degree of reliability of statistical analyses reported on the scientific literature.
Communication skills
The students will have to show ability in supporting their experimental works and studies with appropriate statistical analyses. They will have to prove their skills of communicating others, in a clear and consistent way, the correct interpretation of the results of such analyses, comprehensible even to a non-specialist audience.
Learning skills
The students will have to show the capacity to go beyond the methodologies learned during the course, identifying the quantitative contexts in which more complex and more sophisticated statistical analyses are required. They will have to show an individual and autonomous way of comprehension and application of such new methods.
Prerequisites
Students are expected to be familiar with the basic methodologies of the descriptive statistics, from graphic and table presentation of data to the calculation of basic indicators, besides knowledge of some key theoretical distributions such as Binomial, Poisson and Normal.
Course unit content
The course deals with the most useful and applied statistics in biotechnologic studies, both parametric (t test, ANOVA, linear regression) and non parametric tests (Pearson chi square, Mann-Whitney, McNemar, Kolomogorov-Smirnov). Attention is also devoted to some multivariate methods.
Full programme
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Bibliography
L. Soliani (2015) Statistica di base. Piccin, Padova.
Teaching methods
The course is structured in lectures on different statistical topics. Some practical lectures will address students to learn the use of a freeware statistical package such as PAleontological STatistics: www.nhm.uio.no/norlex/past/download.html.
Open discussion and exchange of ideas between students and the teacher is greatly welcome.
The slides used in the different lectures will be uploaded on the Elly platform at the end of the course. Students should register online to download them.
The slides are an essential part of the teaching material.
Assessment methods and criteria
The assessment method adopted is a written examination. The questions will have the aim of evaluating, first of all, both the knowledge and understanding of the different theoretical topics treated within the course and its application through the interpretation of graphical-statistical outputs provided for the examination. Independent and personal judgement will be evaluated through the comments provided by the student to support his choice of the test (or analysis) more appropriate for a specific research hypothesis. Evaluation of communication skills will be based on the ability to express clear concepts and considerations on experimental and statistical issues, on synthesizing skills, and on the use of an appropriate terminology. Finally, learning skills will be demonstrated through the acquisition of satisfactory levels of personal interpretation, reasoning and independent elaboration of statistical concepts and methodologies.
Other information
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