Filter
Exclude
Time range
-
Minimum likes
Andrew Gelman et al.@StatModeling
14h
“Causal Effects Versus Causal Mechanisms: Two Traditions With Different Requirements and Contributions Towards Causal Understanding”
statmodeling.stat.columbia.edu/2026/11/08/cau…
1
33
168
14,297
Andrew Gelman et al.@StatModeling
Nov 7
2026 compared to other dramatic midterm election reversals of the past half-century
statmodeling.stat.columbia.edu/2026/11/07/202…
0
2
11
2,792
Andrew Gelman et al.@StatModeling
Nov 6
Constructing a latent-variable model to fit measurement data: A Stan story, part 1
statmodeling.stat.columbia.edu/2026/11/06/con…
0
3
28
4,804
Andrew Gelman et al.@StatModeling
Nov 5
Could the 2026 election results lead to a decrease in political polarization?
statmodeling.stat.columbia.edu/2026/11/05/cou…
0
0
4
3,010
Andrew Gelman et al.@StatModeling
Nov 4
“So in that sense the silly statistics in the paper is no big deal, it serves as a sort of rhetorical flourish?”
statmodeling.stat.columbia.edu/2026/11/04/so-…
0
4
7
3,837
Andrew Gelman et al.@StatModeling
Nov 4
Analysis of tonight’s election results (and why I don’t need to do it anymore)
statmodeling.stat.columbia.edu/2026/11/03/dis…
0
4
36
7,471
Andrew Gelman et al.@StatModeling
Nov 3
Survey Statistics: Times/Siena Polls and Times Voter Trends
statmodeling.stat.columbia.edu/2026/11/03/sur…
0
0
5
3,014
Andrew Gelman et al.@StatModeling
Nov 3
Election Day special: Why it can be rational to vote
statmodeling.stat.columbia.edu/2026/11/03/why…
0
1
4
2,702
Andrew Gelman et al.@StatModeling
Nov 3
Alan Zaslavsky
statmodeling.stat.columbia.edu/2026/11/02/ala…
0
1
3
3,097
Andrew Gelman et al.@StatModeling
Nov 2
Science is not naturally aligned
statmodeling.stat.columbia.edu/2026/11/02/sci…
0
2
10
3,385
Andrew Gelman et al.@StatModeling
Nov 2
Most races are so far from close that there’s almost no electoral benefit from moderation. But if you run a lot of extremists all over the country, that could make things harder for your party’s moderates in the few swing states and districts out there.
statmodeling.stat.columbia.edu/2026/11/02/l/
0
0
12
2,913
Andrew Gelman et al.@StatModeling
Nov 1
“Evolutionary psychology hypotheses are testable and falsifiable.”
statmodeling.stat.columbia.edu/2026/11/01/evo…
1
1
23
7,212
Andrew Gelman et al.@StatModeling
Oct 31
Problems with the “Do no harm” principle in the presence of uncertainty
statmodeling.stat.columbia.edu/2026/10/31/pro…
0
1
24
5,487
Andrew Gelman et al.@StatModeling
Oct 31
This guy has a transformer-based election forecasting model
statmodeling.stat.columbia.edu/2026/10/30/thi…
0
2
44
9,998
Andrew Gelman et al.@StatModeling
Oct 30
Stochastic potential outcomes: A discussion with Ang Yu and Jonas Mikhaeil
statmodeling.stat.columbia.edu/2026/10/30/sto…
0
1
5
3,360
Andrew Gelman et al.@StatModeling
Oct 29
R-hat, August 1990
statmodeling.stat.columbia.edu/2026/10/29/r-h…
0
0
6
3,098
Andrew Gelman et al.@StatModeling
Oct 29
“In the run-up to the trial, the brothers conducted an elaborate public relations campaign . . . also arranged for payments to influencers and podcasters who made sympathetic social media posts about the case.”
statmodeling.stat.columbia.edu/2026/10/29/in-…
0
0
1
1,764
Andrew Gelman et al.@StatModeling
Oct 28
Bou-Rabee’s breakthrough paper, “Persistence Pays: Square-root condition-number dependence for Metropolis-adjusted kinetic Langevin Monte Carlo …”
statmodeling.stat.columbia.edu/2026/10/28/bou…
0
1
2
2,006
Andrew Gelman et al.@StatModeling
Oct 28
Tension vs. excitement during NBA games
statmodeling.stat.columbia.edu/2026/10/28/ten…
0
0
5
1,722
Andrew Gelman et al.@StatModeling
Oct 27
We interrupt our usual programming to inform you that I was never hired by Facebook to do a hit job on Jonathan Haidt.
statmodeling.stat.columbia.edu/2026/10/27/we-…
4
42
344
43,551
Load more