Welcome to The Third Summer School on Statistical Methods for Linguistics and Psychology, 2019, 9-13 September

Statistical Methods for Linguistics and Psychology, University of Potsdam, Germany

Application form

Please fill out this form to apply to attend this summer school. Deadling for applications: April 1, 2019. Decisions will be announced April 10, 2019.

Some seats are reserved in each stream for members of the following groups: SFB 1287, SFB 1294, and SFB 1102.

Summer School Location

Griebnitzsee Campus, University of Potsdam, Germany

The summer school will be held at the Griebnitzsee campus of the University of Potsdam; this is about 15-20 minutes away from Berlin zoo station by train. Lectures will be held in Haus 6. Invited lectures will be held in Hoersaal H02. Campus map: download from here. Please use bvg.de for planning your travel (by train or bus).


For previous iterations of this summer school, see the website for SMLP 2017, and SMLP 2018.

Introductory frequentist statistics (maximum 30 participants)

Instructors: Daniel Schad and Audrey Buerki

Topics to be covered:

  • Very basic R usage, basic probability theory, random variables (RVs), including jointly distributed RVs, probability distributions, including bivariate distributions
  • Maximum Likelihood Estimation
  • sampling distribution of mean
  • Null hypothesis significance testing, t-tests, confidence intervals
  • type I error, type II error, power, type M and type S errors
  • An introduction to (generalized) linear models
  • An introduction to linear mixed models

Introductory Bayesian statistics (maximum 30 participants)

Instructors: Shravan Vasishth and Bruno Nicenboim

Topics to be covered:

  • Basic probability theory, random variable (RV) theory, including jointly distributed RVs
  • probability distributions, including bivariate distributions
  • Using Bayes' rule for statistical inference
  • Introduction Markov Chain Monte Carlo
  • Introduction to (generalized) linear models
  • Introduction to hierarchical models
  • Bayesian workflow

Advanced frequentist methods (maximum 30 participants)

Instructors: Reinhold Kliegl, Daniel Schad, and Audrey Buerki

Topics to be covered:

  • Review of linear modeling theory
  • Introduction to linear mixed models
  • Model selection
  • Contrast coding and visualizing partial fixed effects
  • Shrinkage and partial pooling
  • Visualization

Advanced Bayesian methods (maximum 30 participants)

Instructors: Bruno Nicenboim and Shravan Vasishth

Topics will be some selection of the following topics:

  • Review of basic theory
  • Introduction to hierarchical modeling
  • Multinomial processing trees
  • Measurement error models
  • Modeling censored data
  • Meta-analysis
  • Finite mixture models
  • Model selection and hypothesis testing (Bayes factor and k-fold cross-validation)