Basic statistics and experimental design

A.Y. 2020/2021
Overall hours
Learning objectives
The general objective of the course is to provide the basic knowledge, either theoretical or practical, which is fundamental for (1) designing and managing experimental trials, (2) statistically treating the measured data, (3) using dedicated software for statistical processing, and (4) critically discussing the main results of the statistical analyses.
Expected learning outcomes
Students will be able to use dedicated statistical software (e.g., IBM SPSS, R) for processing data from experimental trials. Students will acquire the ability to set up a field or laboratory experiment and read and interpret the results of the statistical analyses.
Course syllabus and organization

Single session

Lesson period
First semester
As we are currently facing the COVI19 pandemic, lessons will be mostly held online, via Microsoft Teams. Please, enter the following code in the Application to find the course Basic Statistics and Experimental Design:


I will create a channel for each lesson within the Team. There, you will find the material in the Post folder. The same material will be stored in this Ariel site (
Course syllabus
Descriptive statistics and sample distributions, statistical tests: Overview of descriptive statistics: central tendency and dispersion indices. Characteristics of populations and samples (references to the most common probability distributions). The estimate of the unknown parameters of a population starting from samples. Distortion, efficiency and consistency of an estimator. The statistical test: concepts of null hypothesis, bilateral and unilateral tests, level of significance, protection, power, errors of I, II and III species. Confidence limits of an average. Techniques for comparisons between sample means: analysis of variance. Prerequisites and conditions of applicability of ANOVA (test of normality and homogeneity of variances). The transformation of data. The analysis of factorial experiments and the interaction: 2 and 3-way anova, and relative interpretation of the results. Multiple comparison techniques between averages (contrasts and post-hoc tests). Introduction to nonparametric analysis of variance techniques. Correlation and regression analysis: The concept of correlation. The correlation coefficient and related statistical tests. Linear regression analysis. The least squares method. Assumptions for regression and related tests. The regression coefficient and its standard error. Significance test for regression and intercept coefficient. Confidence intervals around the regression line. The regression through the origin. The determination coefficient. The analysis of multiple regression. The choice of the optimal model (backward, forward and stepwise regression). Introduction to nonparametric techniques for correlation analysis. Experimental schemes and management of experiments in the field. Experimental randomized block schemes: practical implementation in the field and on the computer, and related statistical analysis procedures.
Prerequisites for admission
Maths, Basic Excel spreadsheet
Teaching methods
The course consists of 32 hours of theoretical lessons and 32 hours of practical lessons, which will be held using dedicated statistics packages (IBM SPSS, R, Excel).
Teaching Resources
The slides are made available during the course.
The following books are recommended (the paragraphs of interest will be highlighted during the course):
Experimental design and data analysis for biologists. Quinn G.P., Keough M.J., Cambridge University Press.
Contemporary statistical models for the plant and soils science. Schabenberger o., Pierce F.J.
Statistics: an introduction using R, 2nd Edition. Crawley M.J., Wiley Edition.
Assessment methods and Criteria
Verification of learning is carried out by examination (on-line or in presence)
The exam will be carried out within a single day and will consist of three parts:
1) Test on the computer for statistical processing of assigned data. An Excel format data set containing data relating to fictitious experiments, briefly described, will be distributed to each student. The student will have to choose the appropriate statistical analysis based on the assigned data; the student will perform the chosen test and briefly comment on the results.
Duration 1 hour.
2) Written exam: 2 open-ended questions and 5 multiple-choice questions. Duration 1 hour and 15 minutes.
3) Short oral test (maximum 10 minutes): discussion of statistical processing, verification of written answers and any questions.
If the examination will be carried out on-line, writing part and short oral will be replaced by an oral examination of about 30'

7 to 8 exam dates are planned annually.
AGR/02 - AGRONOMY AND FIELD CROPS - University credits: 6
Practicals: 32 hours
Lessons: 32 hours
Professor: Perego Alessia