The sociologist and public opinion researcher has a series of excellent posts here, here, and here on the electoral college. Here’s the start: The Electoral College has been in the news recently. I [Weakliem] am going to write a post about public opinion on the Electoral College vs. popular vote, but I was diverted into […]

**Teaching**category.

## Ben Lambert. 2018. *A Student’s Guide to Bayesian Statistics.*

Ben Goodrich, in a Stan forums survey of Stan video lectures, points us to the following book, which introduces Bayes, HMC, and Stan: Ben Lambert. 2018. A Student’s Guide to Bayesian Statistics. SAGE Publications. If Ben Goodrich is recommending it, it’s bound to be good. Amazon reviewers seem to really like it, too. You may […]

## (Markov chain) Monte Carlo doesn’t “explore the posterior”

[Edit: (1) There’s nothing dependent on Markov chain—the argument applies to any Monte Carlo method in high dimensions. (2) No, (MC)MC is not not broken.] First some background, then the bad news, and finally the good news. Spoiler alert: The bad news is that exploring the posterior is intractable; the good news is that we […]

## Kevin Lewis has a surefire idea for a project for the high school Science Talent Search

Here’s his idea: If I were a student, I’d do a study on how Science Talent Search judges are biased. That way, they can’t reject it, otherwise it’s self-confirming. That’s a great idea! Maybe it’s possible to go meta on this one by adding some sort of game-theoretic model or simulation of talent search submission […]

## Statmodeling Retro

As many of you know, this blog auto-posts on twitter. That’s cool. But we also have 15 years of old posts with lots of interesting content and discussion! So I had this idea of setting up another twitter feed, Statmodeling Retro, that would start with our very first post in 2004 and then go forward, […]

## Geoff Pullum, the linguist who hates Strunk and White, is speaking at Columbia this Friday afternoon

The title of the talk is Grammar, Writing Style, and Linguistics, and here’s the abstract: Some critics seem to think that English grammar is just a brief checklist of linguistic table manners that every educated person should already know. Others see grammar as a complex, esoteric, and largely useless discipline replete with technical terms that […]

## Book reading at Ann Arbor Meetup on Monday night: *Probability and Statistics: a simulation-based introduction*

The Talk I’m going to be previewing the book I’m in the process of writing at the Ann Arbor R meetup on Monday. Here are the details, including the working title: Probability and Statistics: a simulation-based introduction Bob Carpenter Monday, February 18, 2019 Ann Arbor SPARK, 330 East Liberty St, Ann Arbor I’ve been to […]

## Should he go to grad school in statistics or computer science?

Someone named Nathan writes: I am an undergraduate student in statistics and a reader of your blog. One thing that you’ve been on about over the past year is the difficulty of executing hypothesis testing correctly, and an apparent desire to see researchers move away from that paradigm. One thing I see you mention several […]

## Wanted: Statistics-related research projects for high school students

So. I sometimes get contacted by high school students who want to work on research projects involving statistics or social science. I’ve supervised several such students, and what works best is when they have their own idea, and I can read what they’ve written and give comments. I’m more of a sounding board than anything […]

## One more reason to remove letters of recommendation when evaluating candidates for jobs or scholarships.

This is just one more sexual harassment story, newsworthy only in the man-bites-dog sense. But it reminded me of something that gets discussed from time to time, which is that we should stop using letters of recommendation to evaluate candidates for jobs or scholarships. Here’s a list of hoops that people recommend you jump through. […]

## Storytelling: What’s it good for?

A story can be an effective way to send a message. Anna Clemens explains: Why are stories so powerful? To answer this, we have to go back at least 100,000 years. This is when humans started to speak. For the following roughly 94,000 years, we could only use spoken words to communicate. Stories helped us […]

## Coursera course on causal inference from Michael Sobel at Columbia

Here’s the description: This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the […]

## MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about

Someone pointed me to this thread where I noticed some issues I’d like to clear up: David Shor: “MRP itself is like, a 2009-era methodology.” Nope. The first paper on MRP was from 1997. And, even then, the component pieces were not new: we were just basically combining two existing ideas from survey sampling: regression […]

## Robin Pemantle’s updated bag of tricks for math teaching!

Here it is! He’s got the following two documents: – Tips for Active Learning in the College Setting – Tips for Active Learning in Teacher Prep or in the K-12 Setting This is great stuff (see my earlier review here). Every mathematician and math teacher in the universe should read this. So, if any of […]

## June is applied regression exam month!

So. I just graded the final exams for our applied regression class. Lots of students made mistakes which gave me the feeling that I didn’t teach the material so well. So I thought it could help lots of people out there if I were to share the questions, solutions, and common errors. It was an […]

## Stephen Wolfram explains neural nets

It’s easy to laugh at Stephen Wolfram, and I don’t like some of his business practices, but he’s an excellent writer and is full of interesting ideas. This long introduction to neural network prediction algorithms is an example. I have no idea if Wolfram wrote this book chapter himself or if he hired one of […]

## Present each others’ posters

It seems that I’ll be judging a poster session next week. So this seems like a good time to repost this from 2009: I was at a conference that had an excellent poster session. I realized the session would have been even better if the students with posters had been randomly assigned to stand next […]

## Don’t get fooled by observational correlations

Gabriel Power writes: Here’s something a little different: clever classrooms, according to which physical characteristics of classrooms cause greater learning. And the effects are large! Moving from the worst to the best design implies a gain of 67% of one year’s worth of learning! Aside from the dubiously large effect size, it looks like the […]

## Columbia Data Science Institute art contest

This is a great idea! Unfortunately, only students at Columbia can submit. I encourage other institutions to do such contests too. We did something similar at Columbia, maybe 10 or 15 years ago? It went well, we just didn’t have the energy to do it again every year, as we’d initially planned. So I’m very […]

## Discussion of effects of growth mindset: Let’s not demand unrealistic effect sizes.

Shreeharsh Kelkar writes: As a regular reader of your blog, I wanted to ask you if you had taken a look at the recent debate about growth mindset [see earlier discussions here and here] that happened on theconversation.com. Here’s the first salvo by Brooke McNamara, and then the response by Carol Dweck herself. The debate […]