The course is the second part of the module Probabilities and Statistics of the international Computer-Science Master (M1 IIT) program. The course mainly targets students and researchers who are interested in experimental research methods and often need to deal with small samples and messy data. Previous knowledge of statistics or probability theory is not required, but some basic understanding of probabilities could help.
The course will introduce fundamental concepts of descriptive and inferential statistics. The goal of the course is NOT to provide a set of statistical recipes or step-by-step instructions. Particular focus will be given on understanding key principles, thinking about underlying assumptions, and recognizing the limitations of statistical methods.
The students will also learn how to use the R statistical software to analyze real datasets and how to apply computational methods to estimate parameters or evaluate statistical procedures.
Classes are given by Theophanis Tsandilas.
I have posted some home exercises to help you prepare for the final exam.
The assignment is out!
Nov 26. Discrete and continuous probability distributions: binomial, normal, log-normal, and chi-square.
The sampling distribution of a statistic. The Central Limit Theorem.
[Lecture 2: Slides] [Lecture 2: R code]
- Lecture notes on the normal distribution and the Central Limit Theorem from MIT. The notes also explain how to use histograms to plot distributions.
- Interactively generate sampling distributions of various statistics from various population distributions.
Jan 7. Significance tests and p values. Type I and Type II errors. Statistical power. Publication bias. P-hacking and criticisms of NHST.
Multiple comparisons. Preregistration.
[Lecture 5: Slides] [Lecture 5: R code]
Jan 21. Preparation for the final exam.
[Lecture 7: Home exercises]