### Week 31b ###

Presenter: Heike Hofmann (Iowa State)
Title: Visual Inference - Examples and Discussion
Abstract: How do you know if something you see in a data plot is really there? Visual inference allows us to find an answer to these questions similar to classical statistical hypothesis testing. Visual inference is based on non-parametric inferential methods using human observers to establish the relevance of graphical findings, thereby creating a bridge between classical statistical inference and exploratory data analysis. Based on the seminal paper by Buja et al (2009), I will start with the lineup protocol, give examples where visual inference has allowed us to gain insight beyond classical modeling situations, and go into a discussion of some of the still open questions on visual inference.
Location: CB04.05.430 (Grid Room)

March 24
### Week 31a ###
March 31
### Week 32 ###