Category: Teaching

David Weakliem on the U.S. electoral college

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 […]

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

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 don’t need to. Sampling to characterize the posterior There’s a misconception among Markov chain Monte Carlo (MCMC) practitioners that the purpose of sampling is to […]

error bars [reposted]

A definitely brilliant entry on xkcd that reflects upon the infinite regress of producing error evaluations that are based on estimates. A must for the next class when I introduce error bars and confidence intervals!

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, […]

I’m getting the point

A long-winded X validated discussion on the [textbook] mean-variance conjugate posterior for the Normal model left me [mildly] depressed at the point and use of answering questions on this forum. Especially as it came at the same time as a catastrophic outcome for my mathematical statistics exam.  Possibly an incentive to quit X validated as […]

I’m getting the point

A long-winded X validated discussion on the [textbook] mean-variance conjugate posterior for the Normal model left me [mildly] depressed at the point and use of answering questions on this forum. Especially as it came at the same time as a catastrophic outcome for my mathematical statistics exam.  Possibly an incentive to quit X validated as […]

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 […]

unbiased estimators that do not exist

When looking at questions on X validated, I came across this seemingly obvious request for an unbiased estimator of P(X=k), when X~B(n,p). Except that X is not observed but only Y~B(s,p) with s<n. Since P(X=k) is a polynomial in p, I was expecting s…

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 […]

The post MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about appeared first on Statistical Modeling, Causal Inference, and Social Science.

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 […]

The post MRP (multilevel regression and poststratification; Mister P): Clearing up misunderstandings about appeared first on Statistical Modeling, Causal Inference, and Social Science.