Peter Vanney, a statistician at Texas Highway Patrol, writes: I’m wondering if you could comment on CURE (CUmulative REsidual) plots that I’m seeing quite a bit in vehicle crash modeling. Ezra Hauer and Joseph Bamfo champion them as a way to determine model fit for their hierarchical Bayesian generalized linear mixed models. I had not […]

**Statistical graphics**category.

## It is somewhat paradoxical that good stories tend to be anomalous, given that when it comes to statistical data, we generally want what is typical, not what is surprising. Our resolution of this paradox is . . .

From a blog comment a few years ago regarding an article by Robert Kosara: As Thomas and I discuss in our paper [When Do Stories Work? Evidence and Illustration in the Social Sciences], it is somewhat paradoxical that good stories tend to be anomalous, given that when it comes to statistical data, we generally want […]

## Applying human factors research to statistical graphics

John Rauser writes: I’ve been a reader of yours (books, papers and the blog) for a long time, and it occurred to me today that I might be able to give something back to you. I recently wrote a talk (https://www.youtube.com/watch?v=fSgEeI2Xpdc) about human factors research applied to making statistical graphics. I mainly cover material from […]

## Graphs as comparisons: A case study

Above is a pair of graphs from a 2015 paper by Alison Gopnik, Thomas Griffiths, and Christopher Lucas. It takes up half a page in the journal, Current Directions in Psychological Science. I think we can do better. First, what’s wrong with the above graphs? We could start with the details: As a reader, I […]

## Question about the secret weapon

Micah Wright writes: I first encountered your explanation of secret weapon plots while I was browsing your blog in grad school, and later in your 2007 book with Jennifer Hill. I found them immediately compelling and intuitive, but I have been met with a lot of confusion and some skepticism when I’ve tried to use […]

## After Peptidegate, a proposed new slogan for PPNAS. And, as a bonus, a fun little graphics project.

Someone pointed me to this post by “Neuroskeptic”: A new paper in the prestigious journal PNAS contains a rather glaring blooper. . . . right there in the abstract, which states that “three neuropeptides (β-endorphin, oxytocin, and dopamine) play particularly important roles” in human sociality. But dopamine is not a neuropeptide. Neither are serotonin or […]

## Hey—here are some tips on communicating data and statistics!

This fall I’ll be again teaching the course, Communicating Data and Statistics. Here’s the rough course plan. I’ll tinker with it between now and September but this is the basic idea. (The course listing is here, but that online description is out of date; the course plan linked above is more accurate.) Here are the […]

## You’ll never guess this one quick trick to diagnose problems with your graphs and then make improvements

The trick is to consider graphs as comparisons. Here’s the story. This post from several years ago shows a confusing and misleading pair of pie charts from a Kenyan election: The quick reaction would be to say, ha ha, pie charts. But that’s not my point here. Sure, pie charts have problems and I think […]

## More graphs of mortality trends

Corinne Riddell writes: In late March you released a series of plots visualizing mortality rates over time by race and gender. For almost a year now, we’ve been working on a similar project and have compiled all of our findings into an R shiny web app here, with a preprint of our first manuscript here. […]

## Click-through graphics: A demonstration visualization project for someone

Hey, read this post. We discuss a shiny information visualization and propose “the click-through solution”: Start with a visually grabby graphic like the one on the linked page, something that takes advantage of some mystery to suck the viewer in. Then click and get a suite of statistical graphs that allow more direct visual comparisons […]

## Mike Bostock graphs federal income tax brackets and tax rates, and I connect to some general principles of statistical graphics

Mike “d3” Bostock writes: Regarding the Vox graph on federal tax brackets, here is a quick-and-dirty visualization of effective tax rates for a given taxable income and year. However, there is a big caveat: estimating the effective tax rate based on actual income is much harder since it depends on the claimed deductions. This could […]

## Taxes and data visualization

Nadia Hassan writes: Vox has a graph of tax rates over time. Their visualizations do convey that tax rates for high earners have declined over time and tax brackets are fewer now, but it seems like there are more appealing and intuitive ways to display that. I agree. This visualization reminds me a lot of […]

## Visualizing your fitted Stan model using ShinyStan without interfering with your Rstudio session

ShinyStan is great, but I don’t always use it because when you call it from R, it freezes up your R session until you close the ShinyStan window. But it turns out that it doesn’t have to be that way. Imad explains: You can open up a new session via the RStudio menu bar (Session […]

## Blue Cross Blue Shield Health Index

Chris Famighetti points us to this page which links to an interactive visualization. There are some problems with the mapping software—when I clicked through, it showed a little map of the western part of the U.S., accompanied by huge swathes of Canada and the Pacific Ocean—and I haven’t taken a look at the methodology. But […]

## Aggregate age-adjusted trends in death rates for non-Hispanic whites and minorities in the U.S.

Following up on our recent Slate article, Jonathan Auerbach made some graphs of mortality rate trends by sex, ethnicity, and age group, aggregating over the entire country. Earlier we’d graphed the trends within each state but there was so much going on there, it was hard to see the big picture. All our graphs are […]

## Easier-to-download graphs of age-adjusted mortality trends by sex, ethnicity, and age group

Jonathan Auerbach and I recently created graphs of smoothed age-adjusted mortality trends from 1999-2014 for: – 50 states – men and women – non-hispanic whites, blacks, and hispanics – age categories 0-1, 1-4, 5-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84. We posted about this on the blog and also wrote an article for Slate […]

## Crack Shot

Raghu Parthasarathy writes: You might find this interesting, an article (and related essay) on the steadily declining percentage of NIH awards going to mid-career scientists and the steadily increasing percentage going to older researchers. The key figure is below. The part that may be of particular interest to you, since you’ve written about age-adjustment in demographic work: does […]

## Some U.S. demographic data at zipcode level conveniently in R

Ari Lamstein writes: I chuckled when I read your recent “R Sucks” post. Some of the comments were a bit … heated … so I thought to send you an email instead. I agree with your point that some of the datasets in R are not particularly relevant. The way that I’ve addressed that is […]

## An exciting new entry in the “clueless graphs from clueless rich guys” competition

Jeff Lax points to this post from Matt Novak linking to a post by Matt Taibbi that shares the above graph from newspaper columnist / rich guy Thomas Friedman. I’m not one to spend precious blog space mocking bad graphs, so I’ll refer you to Novak and Taibbi for the details. One thing I do […]

## “Another terrible plot”

Till Hoffman sent me an email with the above subject line and the following content: These plots from the Daily Mail in the UK probably belong in your hall of fame of terrible visualisations: http://www.dailymail.co.uk/news/article-3655775/The-polls-finally-open-Britain-s-historic-Referendum-vote-latest-polls-Remain-camp-lead-six-points-weather-swing-Brexit.html I was gonna click on this, but then I thought . . . the Daily Mail? Even I have limits […]