Statistics is a must-needed set of tools in experimental sciences. In almost every study, methods from statistics are necessary and their justification is required for a good level publication. Despite the availability of a number of powerful and sophisticated software, it is not always obvious what analysis approach is needed in any particular case if one does not have an introductory level background in the matter. This compact course will cover a few of the fundamentals in statistics.
 
The course will start with a summary of probability theory. In a second part we will move on to cover methods for estimating means and proportions by defining and computing confidence intervals. In a third part we will discuss methods for hypothesis testing for means and proportions using rejection region, p-value and confidence interval approaches. The tools discussed in the class will be the most used ones in everyday work with experimental data (including z-test, t-test). The course won't refer to any particular software but people used to work with any given software are welcome to use it in order to solve the exercises.
 
At the end of the course, each participant will get access to a GitLab repository that contains the lecture notes and several exercise solutions, including scripts in Jupyter notebook.
 
Everyone interested is welcome to join. This course will be available online only and will be delivered live in a compact form, for three days in the row, five hours per day on April 28, 29, 30 from 10am to 4pm (including a lunch break and few more short breaks).
 
Registration to attend this course is compulsory. A link to the course will be sent only to registered participants.