PRINCIPLES OF STATISTICS
Learning outcomes of the course unit
Knowledge and understading
The course aims at providing knowledge and understanding of the classification and graphical representation of scientific data, their distribution and the indices to synthesize it (meand and dispersion). Knowledge and understanding of the theory of probability and how it can be used to test scientific hypotheses through some of the most common statistical tests in the field of Biology, namely Student t test, ANOVA, regression, and so on.
Applying knowledge and understading
The skills provided within the course will enable students to collect, organize and analyze experimental data. In particular, students will be able to plan correctly an experiment, and then applying the fittest test to analyze the data and to provide a correct interpretation of the results.
The skills students will learn during the course will allow them to interpret autonomously experimental data and the potentialities of the various statistical tests, and to form an informed opinion on the statistical analyses likely present in the scientific literature.
The students will have to show ability in supporting their experiments 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.
The students will have to show the capacity to learn new and more complex statistical methods and tests, as well as to identify the scientific and experimental contexts in which they will be applied.
Course contents summary
The first part of the course will concern the basics of statistics. In particular, the student will be introduced to descriptive analyses (measures of central tendency and dispersion) and to theory of probability, including some elements of the most common probability distributions, such as normal, binomial, and Poisson.
The second part will be devoted to the study of inferential statistics. The lessons will focus first on the concepts of sample and universe, and then on the theory of statistical hypothesis testing. These lectures will provide the basic notions to introduce and illustrate the most common statistical tests used in the biological field (t test, ANOVA, chi-square) and also to regression and correlation analyses.
L. Soliani, "Statistica di base", Piccin, Padova.
The lectures will be addressed to provide the theoretical-mathematical background required to comprehend both descriptive and inferential statistics.
Theoretical lectures will be coupled with practical applications of statistical tests with the aim to make the student able to decide how to choose the best statistical test, to practically compute it, and to interpret the final outcome.
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
Final evaluation will be made based on a written examination about both descriptive and inferential statistic. This final exam will be composed of 5 questions, 2 open theoretical questions and 3 exercises. The former will aim at evaluating the knowledge and understanding of the different theoretical topics treated within the course, whilst the latter will evaluate the skills of applying knowledge, tested through the capacity of choosing the fittest test for a specific scientific question. Independent and personal judgement will be evaluated through the comments provided by the student to support his choice of the test (or analysis). Evaluation of communication skills will be based on the ability to express clear concepts and considerations on experimental and statistical issues as well as 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.