Category Archives: Conferences

Tapestry 2016 conference: short stories and wrap-up

My last post introduced the recent Tapestry conference and described the three keynote talks.

Below are my notes on the six “Short Stories” presentations and a few miscellaneous points.

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Tapestry 2016 conference: overview and keynote speakers


Encouraged by Robert Kosara’s call for applications, I attended the Tapestry 2016 conference two weeks ago. As advertised, it was a great chance to meet others from all over the data visualization world. I was one of relatively few academics there, so it was refreshing to chat with journalists, industry analysts, consultants, and so on. (Journalists were especially plentiful since Tapestry is the day before NICAR, the Computer-Assisted Reporting Conference.) Thanks to the presentations, posters & demos, and informal chats throughout the day, I came away with new ideas for improving my dataviz course and my own visualization projects.

I also presented a poster and handout on the course design for my Fall 2015 dataviz class. It was good to get feedback from other people who’ve taught similar courses, especially on the rubrics and assessment side of things.

The conference is organized and sponsored by the folks at Tableau Software. Although I’m an entrenched R user myself, I do appreciate Tableau’s usefulness in bringing the analytic approach of the grammar of graphics to people who aren’t dedicated programmers. To help my students and collaborators, I’ve been meaning to learn to use Tableau better myself. Folks there told me I should join the Pittsburgh Tableau User Group and read Dan Murray’s Tableau Your Data!.

Below are my notes on the three keynote speakers: Scott Klein on the history of data journalism, Jessica Hullman on research into story patterns, and Nick Sousanis on comics and visual thinking vs. traditional text-based scholarship.
My next post will continue with notes on the “short stories” presentations and some miscellaneous thoughts.

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Tapestry 2016 materials: LOs and Rubrics for teaching Statistical Graphics and Visualization

Here are the poster and handout I’ll be presenting tomorrow at the 2016 Tapestry Conference.

Poster "Statistical Graphics and Visualization: Course Learning Objectives and Rubrics"

My poster covers the Learning Objectives that I used to design my dataviz course last fall, along with the grading approach and rubric categories that I used for assessment. The Learning Objectives were a bit unusual for a Statistics department course, emphasizing some topics we teach too rarely (like graphic design). The “specs grading” approach1 seemed to be a success, both for student motivation and for the quality of their final projects.

The handout is a two-sided single page summary of my detailed rubrics for each assignment. By keeping the rubrics broad (and software-agnostic), it should be straightforward to (1) reuse the same basic assignments in future years with different prompts and (2) port these rubrics to dataviz courses in other departments.

I had no luck finding rubrics for these learning objectives when I was designing the course, so I had to write them myself.2 I’m sharing them here in the hopes that other instructors will be able to reuse them—and improve on them!

Any feedback is highly appreciated.


Dataviz contest on “Visualizing Well-Being”

Someone from OECD emailed me about a data visualization contest for the Wikiprogress website (the deadline is August 24th):

Visualizing Well-Being contest

I am contacting you on behalf of the website Wikiprogress, which is currently running a Data Visualization Contest, with the prize of a paid trip to Mexico to attend the 5th OECD World Forum in Guadalajara in October this year. Wikiprogress is an open-source website, hosted by the OECD, to facilitate the exchange of information on well-being and sustainability, and the aim of the competition is to encourage participants to use well-being measurement in innovative ways to a) show how data on well-being give a more meaningful picture of the progress of societies than more traditional growth-oriented approaches, and b) to use their creativity to communicate key ideas about well-being to a broad audience.

After reading your blog, I think that you and your readers might be interested in this challenge. The OECD World Forums bring together hundreds of change-makers from around the world, from world leaders to small, grassroots projects, and the winners will have their work displayed and will be presented with a certificate of recognition during the event.

You can also visit the competition website here:

It does sound like a challenge that might intrigue this blog’s readers:

  • think about how to report human well-being, beyond traditional measures like GDP;
  • find relevant good datasets (“official statistics” or otherwise);
  • visualize these measures’ importance or insightful trends in the data; and
  • possibly win a prize trip to the next OECD forum in Guadalajara, Mexico to network with others who are interested in putting data, statistics, and visualization to good use.

Forget NHST: conference bans all conclusions

Once again, CMU is hosting the illustrious notorious SIGBOVIK conference.

Not to be outdone by the journal editors who banned confidence intervals, the SIGBOVIK 2015 proceedings (p.83) feature a proposal to ban future papers from reporting any conclusions whatsoever:

In other words, from this point forward, BASP papers will only be allowed to include results that “kind of look significant”, but haven’t been vetted by any statistical processes…

This is a bold stance, and I think we, as ACH members, would be remiss if we were to take a stance any less bold. Which is why I propose that SIGBOVIK – from this day forward – should ban conclusions

Of course, even this provision may not be sufficient, since readers may draw their own conclusions from any suggestions, statements, or data presented by authors. Thus, I suggest a phased plan to remove any potential of readers being mislead…

I applaud the author’s courageous leadership. Readers of my own SIGBOVIK 2014 paper on BS inference (with Alex Reinhart) will immediately see the natural synergy between conclusion-free analyses and our own BS.

Belief-Sustaining Inference

TL;DR: If you’re in Pittsburgh today, come to SIGBOVIK 2014 at CMU at 5pm for free food and incredible math!

In a recent chat with my classmate Alex Reinhart, author of Statistics Done Wrong, we noticed a major gap in statistical inference philosophies. Roughly speaking, Bayesian statisticians begin with a prior and a likelihood, while Frequentist statisticians use the likelihood alone. Obviously, there is scope for a philosophy based on the prior alone.

We began to develop this idea, calling it Belief-Sustaining Inference, or BS for short. We discovered that BS inference is extremely efficient, for instance getting by with smaller sample sizes and producing tighter confidence intervals than other inference philosophies.

Today I am proud dismayed complacent to report that our resulting publication has been accepted to the prestigious adequate SIGBOVIK 2014 conference (for topics such as Inept Expert Systems, Artificial Stupidity, and Perplexity Theory):

Reinhart, A. and Wieczorek, J. “Belief-Sustaining Inference.” SIGBOVIK Proceedings, Pittsburgh, PA: Association for Computational Heresy, pp. 77-81, 2014. (pdf)

Our abstract:

Two major paradigms dominate modern statistics: frequentist inference, which uses a likelihood function to objectively draw inferences about the data; and Bayesian methods, which combine the likelihood function with a prior distribution representing the user’s personal beliefs. Besides myriad philosophical disputes, neither method accurately describes how ordinary humans make inferences about data. Personal beliefs clearly color decision-making, contrary to the prescription of frequentism, but many closely-held beliefs do not meet the strict coherence requirements of Bayesian inference. To remedy this problem, we propose belief-sustaining (BS) inference, which makes no use of the data whatsoever, in order to satisfy what we call “the principle of least embarrassment.” This is a much more accurate description of human behavior. We believe this method should replace Bayesian and frequentist inference for economic and public health reasons.

If you’re around CMU today (April 1st), please do stop by SIGBOVIK at 5pm, in Rashid Auditorium in the Gates-Hillman Center. There will be free food, and that’s no joke.

Carl Morris Symposium on Large-Scale Data Inference (3/3)

The final summary of last week’s symposium on statistics and data visualization (see part 1 and part 2)… Below I summarize Chris Volinsky’s talk on city planning with mobile data, and the final panel discussion between the speakers plus additional guests.

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Carl Morris Symposium on Large-Scale Data Inference (2/3)

Continuing the summary of last week’s symposium on statistics and data visualization (see part 1 and part 3)… Here I describe Dianne Cook’s discussion of visual inference, and Rob Kass’ talk on statistics in cognitive neuroscience.

[Edit: I’ve added a few more related links throughout the post.]

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Carl Morris Symposium on Large-Scale Data Inference (1/3)

I enjoyed this week’s Symposium on Large-Scale Data Inference, which honored Harvard’s Carl Morris as the keynote speaker. This was the 2nd such symposium; last year’s honoree was Brad Efron (whose new book I also recommend after seeing it at this event).

This year’s focus was the intersection of statistics and data visualization around the question, “Can we believe what we see?” I was seriously impressed by the variety and quality of the speakers & panelists — many thanks to Social & Scientific Systems for organizing! Look for the lecture videos to be posted online in January.

See below for the first two speakers, Carl Morris and Mark Hansen. The next posts will summarize talks by Di Cook and Rob Kass (part 2), and Chris Volinsky and the final panel discussion (part 3).

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Making R graphics legible in presentation slides

I only visited a few JSM sessions today, as I’ve been focused on preparing for my own talk tomorrow morning. However, I went to several talks in a row which all had a common problem that made me cringe: graphics where the fonts (titles, axes, labels) are too small to read.

You used R's default settings when putting this graph in your slides? Too bad I won't be able to read it from anywhere but the front of the room.

Dear colleagues: if we’re going to the effort of analyzing our data carefully, and creating a lovely graph in R or otherwise to convey our results in a slideshow, let’s PLEASE save our graphs in a way that the text is legible on the slides! If the audience has to strain to read your graphics, it’s no easier to digest than a slide with dense equations or massive tables of numbers.

For those of us working in R, here are some very quick suggestions that would help me focus on the content of your graphics, not on how hard I’m squinting to read them.

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