This came in a mass email: Statistical Horizons is excited to present Applied Bayesian Data Analysis taught by Dr. Roy Levy on Thursday, February 18–Saturday, February 20. In this seminar, you will get both a practical and theoretical introduction to Bayesian methods in just 3 days. Topics include: Model construction Specifying prior distributions Graphical representation […]

**Teaching**category.

## Probability problem involving multiple coronavirus tests in the same household

Mark Tuttle writes: Here is a potential homework problem for your students. The following is a true story. Mid-December, we have a household with five people. My wife and myself, and three who arrived from elsewhere. Subsequently, various diverse symptoms ensue – nothing too serious, but everyone is concerned, obviously. Video conference for all five […]

## Rasslin’ over writin’ teachin’

In an article entitled, “Our Students Can’t Write. We Have Ourselves to Blame,” college professor Robert Zaretsky writes: I, for one, spend my semesters picking through the salads tossed and served up as papers by my students. Consider the opening paragraph from a paper I received this semester. The student, who chose to write on […]

## Reading, practicing, talking, and questioning

Roger Henke writes: I have somewhat of a background in broad strokes policy research. My knowledge of research methodology and stats is very limited and in hindsight I am quite flabbergasted by some of what I’ve claimed in the past based on questionable to say the least data and approaches and equally so by the […]

## Still more on the Heckman Curve!

Carlos Parada writes: Saw your blog post on the Heckman Curve. I went through Heckman’s response that you linked, and it seems to be logically sound but terribly explained, so I feel like I need to explain why Rea+Burton is great empirical work, but it doesn’t actually measure the Heckman curve. The Heckman curve just […]

## When can we challenge authority with authority?

Michael Nelson writes: I want to thank you for posting your last decade of publications in a single space and organized by topic. But I also wanted to share a critique of your argument style as exemplified in your Annals of Surgery correspondence [here and here]. While I think it’s important and valuable that you […]

## Confidence intervals, compatability intervals, uncertainty intervals

“Communicating uncertainty is not just about recognizing its existence; it is also about placing that uncertainty within a larger web of conditional probability statements. . . . No model can include all such factors, thus all forecasts are conditional.” — us (2020). A couple years ago Sander Greenland and I published a discussion about renaming […]

## More background on our research on constructing an informative prior from a corpus of comparable studies

Erik van Zwet writes: The post (“The Shrinkage Trilogy: How to be Bayesian when analyzing simple experiments”) didn’t get as many comments as I’d hoped, so I wrote an short explainer and a reading guide to help people understand what we’re up to. All three papers have the same very simple model. We abstract a […]

## “The 100 Worst Ed-Tech Debacles of the Decade”

This is a list from Audrey Watters (link from Palko). 100! Wow—that’s a long list. But it is for a whole decade. I doubt this’ll make it on to Bill Gates’s must-reads of the year, but I liked it. Just to give you a sense, I’ll share the first and last items on Watters’s list: […]

## “Not statistically significant” is not the same as zero

Under the subject line, “Null misinterpretation of CIs reaches new level of lethality,” Sander Greenland points us to this article with the following in the Results section: Compared to no masks there was no reduction of influenza-like illness (ILI) cases (Risk Ratio 0.93, 95%CI 0.83 to 1.05) or influenza (Risk Ratio 0.84, 95%CI 0.61-1.17) for […]

## If you put an o on understo, you’ll ruin my thunderstorm.

Paul Alper writes: Here is a fascinating article by Matthew Cappucci from the Washington Post dealing with the difficulty experts have when trying to convey technical results to the lay public. In a nutshell, the categories the experts at the Storm Prediction Center use: marginal, slight, enhanced, moderate or high do not correspond to the […]

## Statistical fallacies as they arise in political science (from Bob Jervis)

Bob Jervis sends along this fun document he gives to the students in his classes. Enjoy. Theories of International Relations Assume that all the facts and assertions in these paragraphs are correct. Why do the conclusions not follow? (This does not mean that the conclusions are actually false.) What are the alternative explanations for the […]

## A new idea for a science core course based entirely on computer simulation

Lizzie writes: Thinking on what to do for my now-online stats course next term, I stumbled on your re-post from 2016. And wanted to ask if you or anyone did this (that you know of)? Or if any of your in-development books do lots and lots and lots of simulation? The more I teach fake […]

## The textbook paradox: “Textbooks more than a very few years old cannot even be given away, but new textbooks are mostly made by copying from former ones”

The above remark, from Alan Dunne, applies to mature fields more than to new fields. For example, I guess the textbooks on deep learning are pretty recent, so anything a few years old really would be out of date. Even in subfields that have been around for awhile, it can take a while for textbook […]

## New textbook, “Statistics for Health Data Science,” by Etzioni, Mandel, and Gulati

Ruth Etzioni, Micha Mandel, Roman Gulati wrote a new book that I really like. Here are the chapters: 1 Statistics and Health Data 1.1 Introduction 1.2 Statistics and Organic Statistics 1.3 Statistical Methods and Models 1.4 Health Care Data 1.5 Outline of the Text 1.6 Software and Data 2 Key Statistical Concepts 2.1 Samples and […]

## Thanks, commenters!

The person who sent me this question (“You’re a data scientist at a local hospital and you’ve been asked to present to the physicians on communicating statistical information to patients. What should you say?”) the other day read the comment thread and responded: Thank you so much for putting the question to your readership. Their […]

## You’re a data scientist at a local hospital and you’ve been asked to present to the physicians on communicating statistical information to patients. What should you say?

Someone who wishes to remain anonymous writes: I just read your post reflecting on crappy talks . . . I’m reaching out because I’m a data scientist at a local hospital in the US and I’ve been asked to present to our physicians about communicating statistical information to patients (e.g., how to interpret the results […]

## Reflections on a talk gone wrong

The first talk I ever gave was at a conference in 1988. (This isn’t the one that went wrong.) I spoke on Constrained maximum entropy methods in an image reconstruction problem. The conference was in England, and I learned about it from a wall poster. They had travel funding for students. I sent in my […]

## Sketching the distribution of data vs. sketching the imagined distribution of data

Elliot Marsden writes: I was reading the recently published UK review of food and eating habits. The above figure caught my eye as it looked like the distribution of weight had radically changed, beyond just its mean shifting, over past decades. This would really change my beliefs! But in fact the distributional data wasn’t available […]

## Weakliem on air rage and himmicanes

Weakliem writes: I think I see where the [air rage] analysis went wrong. The dependent variable was whether or not an “air rage” incident happened on the flight. Two important influences on the chance of an incident are the number of passengers and how long the flight was (their data apparently don’t include the number […]