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Archive of posts filed under the Teaching category.

An article in a statistics or medical journal, “Using Simulations to Convince People of the Importance of Random Variation When Interpreting Statistics.”

Andy Stein writes: On one of my projects, I had a plot like the one above of drug concentration vs response, where we divided the patients into 4 groups. I look at the data below and think “wow, these are some wide confidence intervals and random looking data, let’s not spend too much time more […]

“Repeating the experiment” as general advice on data collection

Izzy Kates points to the above excerpt from Introductory Statistics, by Neil Weiss, 9th edition, and points out: Nowhere is repeating the experiment mentioned. This isn’t the only time this mistake is made. Good point! We don’t mention replication as a statistical method in our books either! Even when we talk about the replication crisis, […]

Summer training in statistical sampling at University of Michigan

Yajuan points us to this summer program: The 53rd Sampling Program for Survey Statisticians will be offered by the Survey Research Center at the University of Michigan’s Institute for Social Research from June 3 to July 31, 2020. Founded by Professor Leslie Kish in 1961, the Sampling Program is devoted to training statisticians in sound […]

“It just happens to be in the nature of knowledge that it cannot be conserved if it does not grow.”

Well put.

The 100-day writing challenge

I was looking up Margaret Echelbarger’s email and ended up on her webpage. Margaret has broad interests and I suppose that in the old days she would’ve had a blog, but people don’t do blogs anymore . . . What she does have, though, is a 100-day writing challenge. This seems pretty cool. Is a […]

Fugitive and cloistered virtues

There’s a new revolution, a loud evolution that I saw. Born of confusion and quiet collusion of which mostly I’ve known. — Lana Del Ray While an unexamined life seems like an excellent idea, an unexamined prior distribution probably isn’t. And, I mean, I write and talk and think and talk and write and talk and talk and […]

This graduate student wants to learn statistics to be a better policy analyst

Someone writes: I’m getting a doctoral degree in social science. I previously worked for a data analytics and research organization where I supported policy analysis and strategic planning. I have skills in post-data visualization analysis but am not able to go into an organization, take raw data, and turn it into something usable. I’m planning […]

Do we still recommend average predictive comparisons? Click here to find the surprising answer!

Usually these posts are on 6-month delay but this one’s so quick I thought I’d just post it now . . . Daniel Habermann writes: Do you still like/recommend average predictive comparisons as described in your paper with Iain Pardoe? I [Habermann] find them particularly useful for summarizing logistic regression models. My reply: Yes, I […]

How did our advice about research ethics work out, four years later?

OK, here’s an exam question for you: Someone comes up to you and reminds you that three years ago he asked you for advice, you gave him advice, he followed it, and he’s been doing very well since. You’d like to conclude that your advice helped. Give three reasons why the data conveyed in this […]

Attempts at providing helpful explanations of statistics must avoid instilling misleading or harmful notions: ‘Statistical significance just tells us whether or not something definitely does or definitely doesn’t cause cancer’

This post is by Keith O’Rourke and as with all posts and comments on this blog, is just a deliberation on dealing with uncertainties in scientific inquiry and should not to be attributed to any entity other than the author. As with any critically-thinking inquirer, the views behind these deliberations are always subject to rethinking […]

“Some call it MRP, some Mister P, but the full name is . . .”

Jim Savage points us to this explainer, How do pollsters predict UK general election results?, by John Burn-Murdoch of the Financial Times. It’s bittersweet seeing my method described by some person I’ve never met. Little baby MRP is all grown up! Being explained by the Financial Times—that’s about as good as being in the Guardian […]

No, Bayes does not like Mayor Pete. (Pitfalls of using implied betting market odds to estimate electability.)

Asher Meir points to this amusing post from Greg Mankiw, who writes: Who has the best chance of beating Donald Trump? A clue can be found using Bayes Theorem. Here is the logic. Let A be the event that a candidate wins the general election, and B be the event that a candidate wins his […]

I (inadvertently) misrepresented others’ research in a way that made my story sound better.

During a recent talk (I think it was this one on statistical visualization), I spent a few minutes discussing a political science experiment involving social stimuli and attitudes toward redistribution. I characterized the study as being problematic for various reasons (for background, see this post), and I remarked that you shouldn’t expect to learn much […]

Dow Jones probability calculation

Here’s a cute one for your intro probability class. Karen Langley from the Wall Street Journal asks: What is the probability of the Dow Jones Industrial Average closing unchanged from the day before, as it did yesterday? To answer this question we need to know two things: 1. How much does the Dow Jones average […]

To do: Construct a build-your-own-relevant-statistics-class kit.

Alexis Lerner, who took a couple of our courses on applied regression and communicating data and statistics, designed a new course, “Jews: By the Numbers,” at the University of Toronto: But what does it mean to work with data and statistics in a Jewish studies course? For Lerner, it means not only teaching her students […]

How to teach sensible elementary statistics to lower-division undergraduates?

Kevin Carlson writes: Though my graduate education is in mathematics, I teach elementary statistics to lower-division undergraduates. The traditional elementary statistics curriculum culminates in confidence intervals and hypothesis tests. Most students can learn to perform these tests, but few understand them. It seems to me that there’s a great opportunity to reform the elementary curriculum […]

He’s looking for a Bayesian book

Michael Lewis wrote: I’m teaching a course on Bayesian statistics this fall. I’d love to use your book but think it might be too difficult for the, mainly, graduate social work, sociology, and psychology students likely to enroll. What do you think? In response, I pointed to these two books that are more accessible than […]

The virtue of fake universes: A purposeful and safe way to explain empirical inference.

This post is by Keith O’Rourke and as with all posts and comments on this blog, is just a deliberation on dealing with uncertainties in scientific inquiry and should not to be attributed to any entity other than the author. As with any critically-thinking inquirer, the views behind these deliberations are always subject to rethinking […]

Golf example now a Stan case study!

It’s here! (and here’s the page with all the Stan case studies). In this case study, I’m following up on two earlier posts, here and here, which in turn follow up this 2002 paper with Deb Nolan. My Stan case study is an adaptation of a model fit by Columbia business school professor and golf […]

Jeff Leek: “Data science education as an economic and public health intervention – how statisticians can lead change in the world”

Jeff Leek from Johns Hopkins University is speaking in our statistics department seminar next week: Data science education as an economic and public health intervention – how statisticians can lead change in the world Time: 4:10pm Monday, October 7 Location: 903 School of Social Work Abstract: The data science revolution has led to massive new […]