Statistical principles and biological data analysis

Teacher(s)

Schtickzelle Nicolas;

Language

French

Prerequisites

To follow this course, it is necessary to master the knowledge and skills developed in the course LBIO1282

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.

Main themes

This course lays the foundations of the probabilities and statistics necessary for the analysis of biological data. The topics covered are: probability theory, principles of statistical inference and the main types of basic statistical analysis.
The practical work will allow practical application using the R software.

Learning outcomes

At the end of this learning unit, the student is able to :
1	Calculate and interpret the most common indicators of descriptive statistics, including graphs. Choose the appropriate theoretical distribution to model a random variable. Demonstrate an understanding of the principles of statistical inference. Formulate a hypothesis test in terms of null hypothesis and alternative hypothesis. Choose, perform and interpret a statistical analysis via a basic inference method (t test, analysis of variance, correlation and regression, Xhi²).

Content

With this course, the student acquires the basic notions and principles of probabilities and statistical inference necessary for the scientific process. At the end of the learning phase, they are able to determine the important characteristics of an experimental design, to select and carry out the appropriate statistical analysis for the analysis of the data, and to interpret the results and possible limitations to the conclusions to be drawn.
The course begins with the basics of probability theory. It then details the principles of statistical inference (population vs sample, variables and distributions, sources of variations in the data, hypothesis testing, p-value and type I and II error, confidence interval ...). The main types of basic statistical analysis are detailed and illustrated: t test, ANOVA (1, 2 and 3), correlation and simple linear regression, count data (X²). 
The course is complemented by practical work on computer using the software R, which allows the student to carry out in practice all the statistical analyzes discussed.

Teaching methods

Audience course and practical work in a computer room. The student is encouraged to interactivity for all these activities.
In the event that health regulations do not allow full face-to-face teaching, the course will be broadcast live via Microsoft Teams, either for all students or for a part (while the other part follows the face-to-face course). The course will be as interactive as possible with the possibility for each student to ask their questions live.

Evaluation methods

Open book written exam consisting of multiple choice questions, open questions and practical solution of exercises with R software on a computer. The exam is carried out on Moodle, in a computer room on campus, unless health regulations require that the exam be taken at a distance.

Other information

A basic knowledge of the R software is required: the student is expected to be able to create and modify R-data sets independently. The course LBIO1282 aims specifically to give the student this knowledge; if he has not followed it beforehand, the student must be trained autonomously in these skills, eg by means of the many resources available online for free.

Online resources

The course slides and practical work support material are available on Moodle.
A full set of courses recorded from a previous year are available on Moodle.
Introductory tutorial videos are also available.

Teaching materials

• Visuels du cours disponibles sur Moodle
• Cours d'une année antérieure enregistrés et disponibles sur Moodle

Faculty or entity

BIOL