Vak
2024-2025

Basic statistics: t-test, ANOVA and linear regression.
Basic probability theory: Normal, Binomial and Poisson distributions.

In order to test your statistical knowledge, a test, with accompanying video lectures, will be made available. If the result of the test is unsatisfactory, we advise you to follow the introductory course Basic Statistics for Master students first; please contact the study advisor for further details.

N.B. For the combination master programs (Business Studies, Education and Science Communication) Advanced Statistics is not compulsory. These students can also choose to take Basic Statistics for Master students.

## Description

This course discusses probabilistic theory, experimental design, statistical analysis and statistical modelling in the context of research in the life sciences. Leading concepts in statistics are introduced from the perspective of empirical inquiry and study design. Basic statistics are quickly reviewed and more advanced statistical methods are introduced to deal with data that cannot be analyzed using the standard classical methods:

• Mixed models are introduced to deal with data that are not independent, like repeated and nested designs.

• Generalized models to deal with deviations from normality and heteroscedasticity.

• Machine learning methodologies are discussed to deal with high dimensional data allowing for both prediction and conformational statistics.

• Some of the statistics discussed will be evaluated in the context of bioinformatics.

• In a short detour, we explain important statistical perspectives like the Bayesian view on statistics, information entropy, GAM’s and statistical network theory. These topics might vary depending on interests.

• All statistical examples and assignments are done in R and Rstudio, including simulation of data based on your own experimental designs.

## Course Objectives

After completion of the course, students are able to:
1. Apply methods discussed in GRS/Basic Statistics with extensions to generalized and mixed-model methods, supervised & unsupervised learning methodologies.
2. Identify key data properties of complex study designs from which the student can infer the correct statistical methods to be used and analytic strategies to be followed.
3. Identify statistical pitfalls and fallacies that can occur in statistical analysis.
4. Motivate the use of statistics based on the fundamental principles of a disbalance between the degree of freedom of the model and the data, infer the expected distribution of the residuals and apply this knowledge to the interpretation of statistical results.
5. Deduce and interpret generic mathematical formulas of important statistical concepts.
6. Reason why a particular test statistic takes on certain values under the null hypothesis.
7. Combine statistical data from different literature sources, combine them in a meta-analysis and relate the underlying methodology to mixed models.
8. Convert complex data to tidy format, create subsets and detailed data summaries using a scientific programming language (e.g., R).
9. Simulate and analyze complex data in a scientific programming language (e.g., R).
10. Produce publication-grade figures in a scientific programming language (e.g., R), using basic and advanced plotting routines.

## Timetable

In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.

Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.

Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.

## Mode of instruction

Lectures, tutorials and assignments. Some lectures must be prepared by the students with the use of web lectures and tutorials.

## Assessment method

Written exam and a group assignment.
A weight of 75 % for exam and 25% for the group assignment.

Courses require a minimum, unrounded 5.5 grade to complete.

If a course has 2 or more written partial exams, the minimum grade only applies to the weighted average of the exams.
For partial grades from components other than exams (e.g. practicals, seminars, writing assignments), the bottom grade does apply to the individual components.

Please refer to the Student Charter for an overview of regulations.

## Registration

As a student, you are responsible for enrolling on time through MyStudyMap.

In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.

There are two enrolment periods per year:

• Enrolment for the fall opens in July

• Enrolment for the spring opens in December

Note:

• It is mandatory to enrol for all activities of a course that you are going to follow.

• Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.

• Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.

## Contact

Coordinator: Dr. H.G.J. van Mil
Email: h.g.j.van.mil@umail.leidenuniv.nl

## Remarks

A timetable will be communicated through Brightspace.

Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.