Learning objectives
At the end of the course, the student will have the knowledge and the basic notions to comprehend and apply the classification and representation of scientific data, their distribution as well as the most common measures to represent and describe it (mean and dispersion). The student will learn the theory of probability and will be able to use it for statistical testing of hypothesis through some of the most common statistical tools in the biological field, such as t test, ANOVA, regression analysis, etc.
At the end of the course, the student will be able to use and interpret scientific data correctly, and will know how to choose the best statistical test to analyze both field and laboratory data.
Prerequisites
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Course unit content
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.
Full programme
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Bibliography
L. Soliani, "Statistica applicata", UNINOVA.
Teaching methods
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.
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 by 5 questions, 2 open theoretical questions and 3 exercises.
Other information
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2030 agenda goals for sustainable development
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