At the end of this course unit, students will be able to understand and apply the fundamental principles of probability and statistical inference. They will be able to describe and explore data, reason using probability distributions, construct and interpret confidence intervals, formulate and test statistical hypotheses, perform and interpret simple linear regression, and carry out basic power and sample-size calculations. Students will also be able to select an appropriate statistical method for a simple question in the life sciences, apply it, notably using R, and interpret the results in the context of the problem being studied.
The objective of the course is to provide students with the conceptual foundations needed to understand statistical reasoning, rather than merely applying formulas or procedures. The course aims to develop the ability to translate a scientific question into a statistical problem, understand the role of uncertainty and sampling, select and apply an appropriate method, and critically interpret the resulting output. These skills provide a foundation for data analysis in the life sciences and for more advanced statistics courses.
The course covers descriptive statistics, the principles of probability, the main probability distributions, sampling distributions and confidence intervals. It then introduces the principles of hypothesis testing and their application to one- and two-sample problems, as well as simple linear regression. Finally, statistical power and sample-size calculations are introduced. The theoretical concepts are applied to problems mainly drawn from the life sciences, including through the use of R.
Descriptive statistics: measures of location and variation
Probability and conditional probability
Random variables and probability distributions
Sampling distributions
Estimation and confidence intervals
One-sample hypothesis testing
Two-sample hypothesis testing
Simple linear regression
Statistical power and sample-size calculations
The practical sessions progressively apply the concepts developed in the theoretical course to concrete problems. They include exercises on descriptive statistics and probability, calculations involving different probability distributions, confidence intervals, one- and two-sample hypothesis tests, simple linear regression, and power and sample-size calculations. Particular attention is paid to formulating hypotheses, selecting an appropriate method, checking its assumptions, and interpreting the results in the context of the scientific question. Integrative exercises based on previous examination questions are provided to prepare students for the final assessment.
Teaching combines theoretical classes, individual preparation and practical sessions. Part of the material is prepared independently using the syllabus and resources provided before class. Classes are used to explore concepts in greater depth, answer questions and develop statistical reasoning. Practical sessions focus on problem solving and the application of statistical methods to concrete situations, notably using R.
Attendance at practical sessions is mandatory. Only duly justified absences are accepted. Any timetable conflict must be reported at the beginning of the academic year and before October. Otherwise, absence from a practical session prevents the student from taking the examination.
Assessment consists of two components:
Examination (80%): an examination organised during the examination period, covering material from both the course and the practical sessions. This is an open-book examination: students may bring their syllabus and any other material they consider useful. Internet access is prohibited during the examination, with the exception of the WebCampus platform. Students are strongly encouraged to prepare a concise reference sheet containing, in particular, the main formulas and methods used during the practical sessions.
Continuous assessment (20%): based on class attendance. The grade corresponds to the proportion of classes attended, multiplied by 20. Attendance sheets are distributed at the beginning of each class and must be signed. In the case of a duly justified absence, the corresponding class is excluded from the calculation.
The final grade is the weighted average of the examination (80%) and continuous assessment (20%). To pass the course unit, students must obtain a final grade of at least 10/20 and a grade strictly above 0/20 in each of the two components. A grade of 0/20 in either component automatically results in a final grade of 0/20 for the entire course unit, irrespective of the grade obtained for the other component.
If a student fails the course unit, they must retake during the following examination period the component(s) they failed. The continuous assessment component, however, cannot be retaken: the grade obtained for this component is final for the academic year.
It is therefore possible for a student to fail the course unit despite having passed the examination because of a very low continuous-assessment grade. In this situation, the student must contact the course coordinator to ask whether they may retake the examination and attempt to obtain a sufficiently high grade to reach an overall grade of at least 10/20.
An exemption for a successfully completed component is valid only between examination periods within the same academic year. Partial grades are not transferred to the following academic year.
Students may choose to “sign” an assessment, i.e. not take it, separately for either of the two components. This results in a grade of 0/20 for that component and, in accordance with the rule above, a final grade of 0/20 for the entire course unit. The request must be submitted through SIGALE and confirmed by e-mail to the teaching assistant. Both steps are required.
The theoretical syllabus, exercise syllabus, datasets and other resources required for the course are made available to students online or through institutional platforms. The exercise syllabus also provides solutions to many of the exercises to support independent study. R and RStudio are used for the practical application of many of the statistical methods covered.
| Training | Block | Credits | Mandatory |
|---|---|---|---|
| Bachelor in Geography : General | 2 | 4 | Yes |
| Bachelor in Geology | 2 | 4 | Yes |
| Bachelor in Biology | 2 | 4 | Yes |