Blog · corpus.blog/blogs/statmodeling.stat.columbia.edu/posts
Andrew Gelman
statmodeling.stat.columbia.edu
2020
13 Nov 2020
11 Nov 2020
11 Nov 2020
10 Nov 2020
10 Nov 2020
9 Nov 2020
8 Nov 2020
8 Nov 2020
What would would mean to really take seriously the idea that our forecast probabilities were too far from 50%?original ↗
7 Nov 2020
Don’t kid yourself. The polls messed up—and that would be the case even if we’d forecasted Biden losing Florida and only barely winning the electoral collegeoriginal ↗
4 Nov 2020
4 Nov 2020
3 Nov 2020
3 Nov 2020
So, what’s with that claim that Biden has a 96% chance of winning? (some thoughts with Josh Miller)original ↗
2 Nov 2020
My proposal is to place criticism within the scientific, or social-scientific, enterprise, rather than thinking about it as something coming from outside, or as something that is tacked on at the end.original ↗
31 Oct 2020
27 Oct 2020
27 Oct 2020
Public health researchers explain: “Death by despair” is a thing, but not the biggest thingoriginal ↗
27 Oct 2020
26 Oct 2020
26 Oct 2020
25 Oct 2020
Reverse-engineering the problematic tail behavior of the Fivethirtyeight presidential election forecastoriginal ↗
24 Oct 2020
Merlin did some analysis of possible electoral effects of rejections of vote-by-mail ballots . . .original ↗
24 Oct 2020
“Election Forecasting: How We Succeeded Brilliantly, Failed Miserably, or Landed Somewhere in Between”original ↗
22 Oct 2020
21 Oct 2020
“Model takes many hours to fit and chains don’t converge”: What to do? My advice on first steps.original ↗
21 Oct 2020
Piranhas in the rain: Why instrumental variables are not as clean as you might have thoughtoriginal ↗
20 Oct 2020
Estimated “house effects” (biases of pre-election surveys from different pollsters) and here’s why you have to be careful not to overinterpret them:original ↗
19 Oct 2020
Between-state correlations and weird conditional forecasts: the correlation depends on where you are in the distributionoriginal ↗
17 Oct 2020
Reference for the claim that you need 16 times as much data to estimate interactions as to estimate main effectsoriginal ↗
16 Oct 2020
She’s wary of the consensus based transparency checklist, and here’s a paragraph we should’ve added to that zillion-authored paperoriginal ↗
14 Oct 2020
We are stat professors with the American Statistical Association, and we’re thrilled to talk to you about the statistics behind voting. Ask us anything!original ↗
13 Oct 2020
More on martingale property of probabilistic forecasts and some other issues with our election modeloriginal ↗
12 Oct 2020
“Stop me if you’ve heard this one before: Ivy League law professor writes a deepthoughts think piece explaining a seemingly irrational behavior that doesn’t actually exist.”original ↗
9 Oct 2020
9 Oct 2020
7 Oct 2020
6 Oct 2020
The view that the scientific process is “red tape,” just a bunch of hoops you need to jump through so you can move on with your lifeoriginal ↗
5 Oct 2020
Some wrong lessons people will learn from the president’s illness, hospitalization, and expected recoveryoriginal ↗
4 Oct 2020
It’s kinda like phrenology but worse. Not so good for the “Nature” brand name, huh? Measurement, baby, measurement.original ↗
2 Oct 2020
2 Oct 2020
How to think about extremely unlikely events (such as Biden winning Alabama, Trump winning California, or Biden winning Ohio but losing the election)?original ↗
1 Oct 2020
Randomized but unblinded experiment on vitamin D as a coronavirus treatment. Let’s talk about what comes next. (Hint: it involves multilevel models.)original ↗
30 Sept 2020
27 Sept 2020
26 Sept 2020
24 Sept 2020
(1) The misplaced burden of proof, and (2) selection bias: Two reasons for the persistence of hype in tech and science reportingoriginal ↗
23 Sept 2020
21 Sept 2020
His data came out in the opposite direction of his hypothesis. How to report this in the publication?original ↗
20 Sept 2020
In case you’re wondering . . . this is why the U.S. health care system is the most expensive in the worldoriginal ↗
16 Sept 2020
15 Sept 2020