Programmi dei corsi
 Italiano
 English
BF/0039/EN  BASIS OF SCIENTIFIC METHODOLOGY
Anno Accademico 2021/2022
 Docente

LUCA FRIGAU (Tit.)
ALESSANDRO CAU
 Periodo
 Secondo Semestre
 Modalità d'Erogazione
 Convenzionale
 Lingua Insegnamento
 INGLESE
Informazioni aggiuntive
Corso  Percorso  CFU  Durata(h) 

[60/71] BIOLOGIA CELLULARE E MOLECOLARE  [71/10  Ord. 2021] Advanced cellular studies  7  56 
The aim of the module "Basic Statistics" is to provide students with the basics of biostatistics, to get tools and methodologies useful for data analysis and statistical inference. At the end of the module, the student will know the main indicators used for the descriptive data analysis. Furthermore, the student will be able to figure out what it means to carry out a sample and obtain estimates from statistical inference methods, as well as to use the statistical test tools appropriately.
The module of "Scientific Methodology" aims at providing the students with the basic skills to design a scientific experiment according to a hypothesisbased approach. The students will first learn to distinguish the different explanatory power of correlative vs. hypothesisbased experiments and will be trained on the methods and associate potential biases of different sampling procedures. The course will provide the students with the necessary knowledge to carry out reliable experiments considering the variability patterns of response variables in the field and in the laboratory.
With reference to the Dublin Descriptors, the module aims at providing the student with:
a) Knowledge of the rationale, basic concepts and applicability of inferential statistics and of the different sampling strategies to address scientific questions with either correlative or hypothesisbased approaches;
b) Application capacity by learning of methodological skills with reference to:
i) use of the appropriate basic and hypothesisbased statistical tools and spatialtemporal scales of investigation;
ii) analysis and disentanglement of natural and experimental variability;
iii) assessment of sampling representativity and sampling errors;
iv) choice of different typologies of manipulation experiments and identification of the reference conditions;
c) Autonomy of judgment, through the acquisition of the basic principles of the interpretation of scientific data and statistical tests output; design of uni and multivariate manipulation experiments for addressing biological questions;
d) Ability in communication, through the acquisition of appropriate terminological background and the use of appropriate graphic representations of multivariate statistical analyses;
e) Ability to choose the most appropriate statistical tools in different research scenarios.
Concepts and methods developed during the Mathematics and Statistics course.
Module of Basic Statistics:
 Scale of measurement
 Graphical representation of data
 Measures of central tendency
 Variability measures
 Indexes of form
 Linear correlation and Linear regression.
 Random variables: Normal, Binomial, Chisquare, Student's T random, Fisher's F
 The concepts of Statistical hypothesis testing
 Hypothesis tests: Chisquare Test, Fisher's exact method, Loglikelihood ratio, Binomial Proportion Test, McNemar Test, Ttests, Wilcoxon rank Test, Welch’s Test, MannWhitney U Test, Paired Samples Ttests, Anova I, Anova II
 Adjusting significance levels and/or P values in multiple testing
 Resampling methods for estimation: Bootstrap, Jackknife
Module of Scientific Methodology:
 Differences between experiments and monitoring procedures
 Multivariate nature of variation sources of biological systems
 Spatial and temporal scales of biological variation
 Definition and classification of scientific experiments
 Natural variability of biological processes and the need of sampling
 Explanatory power of correlative and hypothesisbased experiments
 Representative sampling and allocation in time and space
 Sampling methods: convenience, random, systematic, stratified and cluster sampling
 Sampling errors: sample size, systematic errors, selection biases, measurement biases, confounding factors
 Variability appreciation
 Design of univariate and multifactorial experiments
 Experimental factors and levels
 Orthogonal and hierarchical factors in multifactorial experiments
 Choice of reliable statistical tools to address experimental questions
Lectures, seminars, practical exercises in the classroom.
The lectures will be given mainly in presence, integrated and "augmented" with online strategies, in order to guarantee its use in an innovative and inclusive way
The evaluation of each module is based upon a written exam that will consider the overall appropriateness, accuracy and consistency of the acquired knowledge, skills, and competencies. Evaluation will be made also considering the capacity of expression and the proper use of the disciplinespecific language as well as the logical ability in the consequential fitting of the module contents by connecting different subjects and summarizing concepts through the graphic expression of ideas and concepts (for example by means of schemes of sampling strategy and experimental design).
The final evaluation results from the weighted average of the evaluations of the two modules. Specifically, the modules of Basic Statistics and Scientific Methodology weigh, respectively, 4/7 and 3/7.
Relational qualities:
Ability to talk and interact with critical spirit with the teacher and to selfevaluation
Consequently, the judgment can be:
a) Fair (18 to 20/30)
The candidate demonstrates little knowledge acquired, superficial level, many gaps. Expressive abilities modest, but still sufficient to support a coherent dialogue, logical and consequential in the fitting of the subjects of the elementary level; poor capacity for synthesis and ability to graphic expression rather stunted, lack of interaction with the examiner.
b) Moderate (21 to 23)
The applicant demonstrates a discreet acquisition of knowledge but lack of depth, a few gaps; expressive abilities more than sufficient to support a coherent dialogue; acceptable mastery of the language of science, logical and consequential in the fitting of the arguments of moderate complexity, more than enough capacity for synthesis and ability to graphic expression acceptable.
c) Good (24 to 26)
The candidate demonstrates knowledge rather large, moderate depth, with few gaps; satisfactory mastery of the expressive capabilities and significant scientific language; critical ability, good capacity for synthesis and ability to graphic expression more than acceptable.
d) Outstanding (27 to 29)
The candidate demonstrates a wealth of notions very extensive, well depth, with marginal gaps; remarkable powers of expression and high mastery of scientific language; remarkable dialogue capacity, good competence and relevant aptitude for logic synthesis, high capacity for synthesis and graphic expression.
e) Excellent (30)
The candidate demonstrates a wealth of very extensive and indepth knowledge, gaps irrelevant, high capacity and high mastery of the expressive language of science; excellent ability dialogical aptitude to make connections between different subjects, excellent ability to synthesize and very familiar with the expression graphics.
The praise is attributed to the candidates clearly above average, and whose notional limits, if any, expressive, conceptual, logical, as a whole are completely irrelevant.
 Experimental Design and Data Analysis for Biologists, G.P. Quinn, M.J. Keough
 Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance, A.J. Underwood
Students will be given slides of lectures, scientific publications (reviews) on specific topics covered in class (English)