Christophe Lalanne
September 29th, 2015

What you will learn

This course is a short and practical introduction to statistical analysis of experimental data.

  • We will not cover mathematical statistics, but we will focus on applied statistics.
  • We will not provide a complete account of statistical modeling.
  • Examples will come from various disciplines: experimental psychology, medicine, biomedical engineering.
  • We will try to emphasize the interpretation of results, and how to get them right.


  • Time: 9:00–11:00, every Tuesday
  • Prerequisites: basic math and experimental design (AMS, previous exposition, or more?)
  • Statistical packages: R (sessions 1 to 7), Python (sessions 8 to 11)
  • Resources: Handouts available on Github, open access textbook
  • Homeworks: short quizzes to complete at home
  • Grading: assignements + final project



Christophe Lalanne [R], Sylvain Charron [Py].

  1. Introduction to R and data analysis (Sep. 29)
  2. Descriptive statistics, association measures (Oct. 6)
  3. Inferential statistics and two-group comparisons (Oct. 13)
  4. Experimental design and ANOVA (Oct. 20)
  5. Simple and multiple linear regression (Nov. 3)
  6. The linear model and its applications (Nov. 10)
  7. Categorical data and two-ways tables (Nov. 17)

Sessions 8 to 11 with Python will consist in replicating analyses done in R.


OpenIntro Statistics

  • 10 to 15 pages to read before each course
  • Additional information will be given during the course
  • Topics not covered in the textbook will be detailed on the course homepage


Textbook (2)

Learning Statistics with R

Once more

  • 10 to 15 pages to read
  • Focus on statistics applied to psy research