Distributions & Uncertainty

Materials for class on Monday, February 18, 2019


Tweet of the Day:

Matthew Kay is an assistant professor of Information at the University of Michigan School of Information. His research focuses on communicating uncertainty, especially from a Bayesian perspective including authoring the tidybayes package. If you’re new to Bayesian statistics, he has an excellent paper on why Bayesian statistics is appropriate for human-centered (HCI) research. I also highly recommend his research appearance on the DataStories podcast with his colleague, Jessica Hullman.


full screen / pdf version

Matthew Kay’s Tidy data and Bayesian analysis make uncertainty visualization fun

Kristoffer Magnusson’s visualization demos:

StackOverflow: Difference between Bayesian and BoostrappingOr if you want even more technical, see the Wikipedia page on Expected Loss. This gets at the core difference between Bayesian and Frequentist schools of thought.

Pierre Dragicevic’s “Fair statistical communication in HCI” paper.This paper is an excellent guide for ways to appropriately communicate statistical models and uncertainty. This provides a good background on why overemphasis on p-values and dichotomous testing can go wrong and miss important perspectives when doing hypothesis testing.


Claus Wilke’s 2019 RStudio::Conf talk on Visualizing uncertainty with hypothetical outcome plots (HOPs) / GitHub Code / ungeviz packageYou can watch Claus’ talk here!

For the lab session, update last week’s gganimate folder on our RStudio.cloud course page.

If we have time, I also included Matthew Kay’s Uncertainty visualization examples in the RStudio.cloud project (see uncertainty-examples folder).