Biostatistics and Design of Experiments in Biotechnology
A.Y. 2026/2027
Learning objectives
The teaching aims to equip students with essential knowledge and skills to effectively describe and analyze biological and biotechnological data using appropriate statistical methods. It combines a solid theoretical framework with practical applications in descriptive and inferential statistics, including hypothesis testing. Additionally, the teaching introduces the fundamental principles of common experimental designs. Alongside lectures, significant emphasis will be placed on hands-on computer-based exercises.
Expected learning outcomes
Students will develop the following skills:
1) organize, summarize, and visually represent biotechnological data;
2) select and apply appropriate standard errors and confidence intervals for key statistical measures;
3) formulate hypotheses and perform suitable statistical tests;
4) identify and design appropriate experimental frameworks;
5) interpret and critically evaluate the results produced by statistical software.
1) organize, summarize, and visually represent biotechnological data;
2) select and apply appropriate standard errors and confidence intervals for key statistical measures;
3) formulate hypotheses and perform suitable statistical tests;
4) identify and design appropriate experimental frameworks;
5) interpret and critically evaluate the results produced by statistical software.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
The program is the same for both attending and non-attending students. Assiduous attendance at the course is strongly recommended.
Program
INTRODUCTION: DESCRIPTIVE AND INFERENTIAL STATISTICS: Samples and populations. Types of variables: qualitative, quantitative. Precision and accuracy.
Theoretical class: 2 hrs
DATA VISUALIZATION: Absolute, relative and cumulative frequency tables. Representing frequency distributions, diagrams and histograms, percentiles and quantiles, contingency tables. Scatterplots, line charts, maps.
Theoretical class: 3 hrs - Practical class: 2 hrs
DESCRIBING DATA: Measures of location and dispersion, geometric and arithmetic mean, median, mode, interquartile range, range of variation, deviation, variance, standard deviation, coefficient of variation.
Theoretical class: 3 hrs - Practical class: 3 hrs
ESTIMATION WITH UNCERTAINTY: Sampling distribution, standard error and confidence interval.
Theoretical class: 2 hrs - Practical class: 2 hrs
PROBABILITY: Basic rules, Venn diagrams, probability trees, odds, independence and the product rule, conditional probability.
Theoretical class: 2 hrs - Practical class: 1 hr
HYPOTHESIS TESTING: Null and alternative hypotheses, statistical significance, P-value, hypothesis testing and confidence intervals, Type I and Type II errors.
Theoretical class: 3 hrs - Practical class: 1 hr
ANALYSIS OF PROPORTIONS: Binomial distribution. Estimation of proportions: confidence interval and standard error of a proportion. Chi-square test and goodness of fit. Poisson distribution. Contingency tables and chi-square test for categorical variables.
Theoretical class: 4 hrs - Practical class: 3 hrs
NORMAL DISTRIBUTION: Formula, assumptions and properties. Central limit theorem. Normal approximation for binomial distribution.
Theoretical class: 3 hrs - Practical class: 2 hrs
INFERENCE IN POPULATIONS WITH A NORMAL DISTRIBUTION: t-distribution, assumptions and properties. One-sample t-test. Comparison between two means. Paired comparison between means. Comparison of means from two samples.
Theoretical class: 3 hrs - Practical class: 2 hrs
ANALYSIS OF VARIANCE: Comparisons between means of multiple groups.
Theoretical class: 3 hrs - Practical class: 2 hrs
MEASUREMENTS OF RELATIONSHIP BETWEEN VARIABLES: Covariance, correlation and linear regression. Least squares method and linear regression.
Theoretical class: 4 hrs - Practical class: 2 hrs
GUIDELINES FOR DESIGNING EXPERIMENTS: The three basic principles of experimental design: randomization, replication and blocking. Determining sample size.
Theoretical class: 4 hrs - Practical class: 1 hr
ONLINE SYNCHRONOUS REVIEW AND INTEGRATION SESSIONS (Microsoft Teams): Review and integration of the topics covered during the course, discussion of practical applications, clarification of statistical concepts, and guided problem-solving activities aimed at consolidating learning outcomes. The online format is intended to facilitate the participation and interaction of both attending and non-attending students.
Online synchronous activities: 6 hrs
Program
INTRODUCTION: DESCRIPTIVE AND INFERENTIAL STATISTICS: Samples and populations. Types of variables: qualitative, quantitative. Precision and accuracy.
Theoretical class: 2 hrs
DATA VISUALIZATION: Absolute, relative and cumulative frequency tables. Representing frequency distributions, diagrams and histograms, percentiles and quantiles, contingency tables. Scatterplots, line charts, maps.
Theoretical class: 3 hrs - Practical class: 2 hrs
DESCRIBING DATA: Measures of location and dispersion, geometric and arithmetic mean, median, mode, interquartile range, range of variation, deviation, variance, standard deviation, coefficient of variation.
Theoretical class: 3 hrs - Practical class: 3 hrs
ESTIMATION WITH UNCERTAINTY: Sampling distribution, standard error and confidence interval.
Theoretical class: 2 hrs - Practical class: 2 hrs
PROBABILITY: Basic rules, Venn diagrams, probability trees, odds, independence and the product rule, conditional probability.
Theoretical class: 2 hrs - Practical class: 1 hr
HYPOTHESIS TESTING: Null and alternative hypotheses, statistical significance, P-value, hypothesis testing and confidence intervals, Type I and Type II errors.
Theoretical class: 3 hrs - Practical class: 1 hr
ANALYSIS OF PROPORTIONS: Binomial distribution. Estimation of proportions: confidence interval and standard error of a proportion. Chi-square test and goodness of fit. Poisson distribution. Contingency tables and chi-square test for categorical variables.
Theoretical class: 4 hrs - Practical class: 3 hrs
NORMAL DISTRIBUTION: Formula, assumptions and properties. Central limit theorem. Normal approximation for binomial distribution.
Theoretical class: 3 hrs - Practical class: 2 hrs
INFERENCE IN POPULATIONS WITH A NORMAL DISTRIBUTION: t-distribution, assumptions and properties. One-sample t-test. Comparison between two means. Paired comparison between means. Comparison of means from two samples.
Theoretical class: 3 hrs - Practical class: 2 hrs
ANALYSIS OF VARIANCE: Comparisons between means of multiple groups.
Theoretical class: 3 hrs - Practical class: 2 hrs
MEASUREMENTS OF RELATIONSHIP BETWEEN VARIABLES: Covariance, correlation and linear regression. Least squares method and linear regression.
Theoretical class: 4 hrs - Practical class: 2 hrs
GUIDELINES FOR DESIGNING EXPERIMENTS: The three basic principles of experimental design: randomization, replication and blocking. Determining sample size.
Theoretical class: 4 hrs - Practical class: 1 hr
ONLINE SYNCHRONOUS REVIEW AND INTEGRATION SESSIONS (Microsoft Teams): Review and integration of the topics covered during the course, discussion of practical applications, clarification of statistical concepts, and guided problem-solving activities aimed at consolidating learning outcomes. The online format is intended to facilitate the participation and interaction of both attending and non-attending students.
Online synchronous activities: 6 hrs
Prerequisites for admission
Basic knowledge of mathematics.
Basic knowledge of the office suite.
There is no need for a personal computer: classes are held in a computer classroom with PCs available to students
Basic knowledge of the office suite.
There is no need for a personal computer: classes are held in a computer classroom with PCs available to students
Teaching methods
Besides theoretical classes (4.5 CFU, 32 hours), close attention will be paid to computer sessions (1.5 CFU, 24 hours). Biostatistics will be presented with a practical approach emphasizing the rationale of statistical theory and methods rather than mathematical proofs and formalisms. Each theoretical lecture will be combined with practical applications and exercises using simple spreadsheets to analyze experimental data. Besides theoretical classes, there will also be intensive computer sessions.
Approximately 6 hours (about 10% of the total teaching activities) will be delivered online in synchronous mode via Microsoft Teams at the end of the course. These sessions will be dedicated to reviewing, integrating, and discussing the different topics covered throughout the course, fostering connections among concepts and supporting students in consolidating the acquired knowledge and skills. The online format is also intended to facilitate the participation and interaction of both attending and non-attending students, providing an additional opportunity for discussion, clarification, and course content integration.
Students can choose to attend this course during the first year (I semester) or the second year (I semester). As the skills acquired from this teaching are useful for experimental internship activities, students are warmly encouraged to attend the course in the first year (I semester)
Approximately 6 hours (about 10% of the total teaching activities) will be delivered online in synchronous mode via Microsoft Teams at the end of the course. These sessions will be dedicated to reviewing, integrating, and discussing the different topics covered throughout the course, fostering connections among concepts and supporting students in consolidating the acquired knowledge and skills. The online format is also intended to facilitate the participation and interaction of both attending and non-attending students, providing an additional opportunity for discussion, clarification, and course content integration.
Students can choose to attend this course during the first year (I semester) or the second year (I semester). As the skills acquired from this teaching are useful for experimental internship activities, students are warmly encouraged to attend the course in the first year (I semester)
Teaching Resources
Slides of any lecture, exercises, datasets, procedures for data analysis and bibliographic material will be provided by the teacher through the Ariel online platform.
The core text is: The analysis of Biological data - Second edition. Whitlock M.C. Schluter D.
Ed. By W.H. Freeman and company
Other reference books will be suggested during the course.
The material and suggested books are the same for both attending and nonattending students
The core text is: The analysis of Biological data - Second edition. Whitlock M.C. Schluter D.
Ed. By W.H. Freeman and company
Other reference books will be suggested during the course.
The material and suggested books are the same for both attending and nonattending students
Assessment methods and Criteria
The exam consists of a written and a practical examination. The written part consists of 6 questions (1 multiple choice question, 2 open theoretical questions; 2 practical problems; 1 statistical analysis to comment on) - points 0-4 for each question, and a computer session with 1 dataset to analyze. The biostatistical test will assess the ability to organize, summarize, and represent biotechnological data by choosing the appropriate experimental methodologies and statistical tests. The final score will be the summation of the points obtained in the different parts (written part , points 0-4 for each questions, computer session points 0-6), aimed at ascertaining:
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of statistical analysis by commenting on an analytical output of statistical software used during the course and your ability in organize an experimental plan.
The final evaluation is based on a total of 30 points.
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
The results of the exam will be communicate to the students via the official site of the course (my ariel) or by email.
Examination mode is the same for both attending and non-attending students
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of statistical analysis by commenting on an analytical output of statistical software used during the course and your ability in organize an experimental plan.
The final evaluation is based on a total of 30 points.
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
The results of the exam will be communicate to the students via the official site of the course (my ariel) or by email.
Examination mode is the same for both attending and non-attending students
AGR/17 - LIVESTOCK SYSTEMS, ANIMAL BREEDING AND GENETICS - University credits: 6
Computer classroom exercises : 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Crepaldi Paola
Shifts:
Turno
Professor:
Crepaldi PaolaProfessor(s)
Reception:
keeping an appointment by e-mail
Sezione di Zootecnica Agraria, 1st floor, Via Celoria 2