
Michael A. Bailey
Professor of Political Science
1
APPEARANCES
1
PODCASTS
012
DEC 30
JAN 6
JAN 13
JAN 20
JAN 27
FEB 3
FEB 10
FEB 17
FEB 24
MAR 3
MAR 10
MAR 17
MAR 24
MAR 31
APR 7
APR 14
APR 21
APR 28
MAY 5
MAY 12
MAY 19
MAY 26
JUN 2
JUN 9
JUN 16
JUN 23
JUN 30
JUL 7
JUL 14
JUL 21
JUL 28
AUG 4
AUG 11
AUG 18
AUG 25
SEP 1
SEP 8
SEP 15
SEP 22
SEP 29
OCT 6
OCT 13
OCT 20
OCT 27
NOV 3
NOV 10
NOV 17
NOV 24
DEC 1
DEC 8
DEC 15
DEC 22
DEC 29
JAN 5
JAN 12
JAN 19
JAN 26
FEB 2
FEB 9
FEB 16
FEB 23
MAR 2
MAR 9
MAR 16
MAR 23
MAR 30
APR 6
APR 13
APR 20
APR 27
MAY 4
MAY 11
MAY 18
MAY 25
JUN 1
JUN 8
JUN 15
JUN 22
JUN 29
JUL 6
JUL 13
JUL 20
JUL 27
AUG 3
AUG 10
AUG 17
AUG 24
AUG 31
SEP 7
SEP 14
SEP 21
SEP 28
OCT 5
OCT 12
Oct 1, 2026
From Polling to Prediction Markets: How Reliable Can We Predict Elections?
5:35
5:46
5:59
6:07
6:16
12:16

Xiao-Li MengHOST
How did that problem show up in this cycle? Did the pollster handle it better or worse than- The last three elections now, 2016, 2020, 2024, every election we see this problem persistent.

Michael A. BaileyGUEST
Yeah, I mean, basically, as you alluded to, the, the polling paradigm of calling 1,000 people, they all answer, and then you ask them who they're gonna vote for, that, like, is amazing.

Michael A. BaileyGUEST
It's unbiased, and we can exactly characterize, uh, the degrees of confidence about it.

Michael A. BaileyGUEST
You know, maybe one pollster at The New York Times tries to do that, but again, as you guys know, they're getting 1% response rate, and they're on the high end [chuckles] of things.

Michael A. BaileyGUEST
And, by the way, like, the, um-- and this will get in as we talk more about weighting.
6 MINS LATER

Xiao-Li MengHOST
So these things can change over time, and we need to tell people that we are capturing part of that dynamic and that it's g- ongoing, and that's part of the story.