Admission requirements
It is recommended that the student has successfully completed Probability for Computer Scientists before starting this course.
Description
Statistics is the science concerning the description and analysis of data; with the aim to draw generally valid conclusions. Statistics forms the core of many methods in data science and artificial intelligence; making it an essential foundation for other courses (e.g.; machine learning; data mining).
The focus of the course is on thoroughly understanding and correctly applying statistical methods; not on the formal justification or derivation of those methods. We consider both descriptive statistics; i.e.; methods for describing a given collection of data; and inferential statistics; i.e.; methods for inferring properties of a population based on a limited yet respresentative sample.
The course introduces the necessary basic concepts (probability; random variables; statistics; parameters; probability distributions; inference; point and interval estimates; hypothesis testing); various inference methods for specific parameters (e.g.; for a single mean or proportion; for two samples; for correlelation; ...); and methods for constructing predictive models (linear regression).
Course objectives
At the end of the course, students should be able to:
Summarise a dataset using appropriate descriptive statistics by hand and using JASP (including numeric ones such as the mean, median, mode, and standard deviation, and graphical ones such as bar plots, scatter plots, and box plots).
Explain various data collection methods (such as simple random, cluster, stratified, and multistage sampling), and their potential forms of bias (including selection, response, and non-responsed bias).
Explain the central limit theorem, the concepts of hypothesis testing, hypothesis pairs, Type I and Type II errors, and model selection.
Identify the appropriate statistical method (from estimation and hypothesis testing) for a research question.
Perform various estimates and parametric statistical tests by hand and using JASP software, including point estimates, confidence intervals, and one-sample and two-sample tests for a mean and proportion, the Chi-square test, and F-test.
Construct a (simple and multiple) linear regression model, perform simple model selection (AIC, BIC), interpret its results, and test for independence.
Discuss limitations of statistical inference and especially statistical hypothesis testing, including the concepts of p-hacking and publication bias.
Explain (and interpret the results of) various nonparametric statistical methods, including the bootstrap, the permutation test for independence, Wilcoxon nonparametric test, Kruskall-Wallis test, and Friedman test.
Schedule
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.
Additionally; you can easily link MyTimetable to a calendar app on your phone; and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
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.
Teaching method
One 2-hour lecture and one 2-hour tutorial session per week.
Assesment method
The course grade will be calculated as follows:
Two individual JASP assignments via Ans - 15% each, 30% in total
Final written exam (closed-book) - 70%
There will be one optional written assignment at the end of the semester, similar to the written exam. If this assignment is submitted and the grade is higher than the grade for the final exam, it will count for 10% of the exam grade.
I.e., the formula for the final grade F is: F = 0.3 A + 0.7 max(E, 0.1 W + 0.9 E),
where A is the average of the grades for the two JASP assignments, E is the grade for the written exam, and W is the grade for the optional written assignment.
A student can only pass the course if the partial grades for (1) the assignments and (2) written exam are both at least 5.5. That is, it is required that both A >= 5.5 and E >= 5.5. If either constraint is not satisfied, then F = min(A, E).
Resit, review & feedback
Both JASP assignments and the final exam have one resit opportunity each. There is no resit opportunity for the optional written assignment. The teacher will inform the students how the inspection of and follow-up discussion of the assignments and exams will take place upon publication of the grades.
Reading list
Statistics: The Art and Science of Learning from Data, Global Edition – Fifth Edition, Alan Agresti, Pearson Education, ISBN 9781292444765. Having a physical copy of the book is highly recommended.
The slides that will be published via Brightspace.
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
See this page for more information about deadlines and enrolling for courses and exams.
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.