An anonymous group of authors writes: Political situation Belarus is often called the “last dictatorship” in Europe. Rightly so, Aliaskandr Lukashenka has served as the country’s president since 1994. In the 26 years of his rule, Lukashenka has consolidated and extended his power, which is today absolute. Rigging referendums has been an effective means of […]
Archive of posts filed under the Political Science category.
Is vs. ought in the study of public opinion: Coronavirus “opening up” edition
I came across this argument between two of my former co-bloggers which illustrates a general difficulty when thinking about political attitudes, which is confusion between two things: (a) public opinion, and (b) what we want public opinion to be. This is something I’ve been thinking about for many years, ever since our Red State Blue […]
What went wrong with the polls in 2020? Another example.
Shortly before the election the New York Times ran this article, “The One Pollster in America Who Is Sure Trump Is Going to Win,” featuring Robert Cahaly, who on election day forecast Biden to win 235 electoral votes. As you may have heard, Biden actually won 306. Our Economist model gave a final prediction of […]
The rise and fall and rise of randomized controlled trials (RCTs) in international development
Gil Eyal sends along this fascinating paper coauthored with Luciana de Souza Leão, “The rise of randomized controlled trials (RCTs) in international development in historical perspective.” Here’s the story: Although the buzz around RCT evaluations dates from the 2000s, we show that what we are witnessing now is a second wave of RCTs, while a […]
Can we stop talking about how we’re better off without election forecasting?
This is a public service post of sorts, meant to collect some reasons why getting rid of election forecasts is a non-starter in one place. First to set context: what are the reasons people argue we should give them up? This is far from an exhaustive list (and some of these reasons overlap) but a […]
Lying with statistics
As Deb Nolan and I wrote in our book, Teaching Statistics: A Bag of Tricks, the most basic form of lying with statistics is simply to make up a number. We gave the example of Senator McCarthy’s proclaimed (but nonexistent) list of 205 Communists, but we have a more recent example: One of the supposed […]
What happens to the median voter when the electoral median is at 52/48 rather than 50/50?
Here’s a political science research project for you. Joe Biden got about 52 or 53% of the two-party vote, which was enough for him to get a pretty close win in the electoral college. As we’ve discussed, 52-48 is a close win by historical or international standards but a reasonably big win in the context […]
UX issues around voting
While Andrew’s worrying about how to measure calibration and sharpness on small N probabilistic predictions, let’s consider some computer and cognitive science issues around voting. How well do elections measure individual voter intent? What is the probability that a voter who tries to vote has their intended votes across the ballot registered? Spoiler alert. It’s […]
Stop-and-frisk data
People sometimes ask us for the data from our article on stop-and-frisk policing, but for legal reasons these data cannot be shared. Other data are available, though. Sharad Goel writes: You might also check out stop-and-frisk data from Chicago and Seattle. And, if you’re interested in traffic stop data as well, see our Open Policing […]
What would would mean to really take seriously the idea that our forecast probabilities were too far from 50%?
Here’s something I’ve been chewing on that I’m still working through. Suppose our forecast in a certain state is that candidate X will win 0.52 of the two-party vote, with a forecast standard deviation of 0.02. Suppose also that the forecast has a normal distribution. (We’ve talked about the possible advantages of long-tailed forecasts, but […]
Is there a middle ground in communicating uncertainty in election forecasts?
Beyond razing forecasting to the ground, over the last few days there’s been renewed discussion online about how election forecast communication again failed the public. I’m not convinced there are easy answers here, but it’s worth considering some of the possible avenues forward. Let’s put aside any possibility of not doing forecasts, and assume the […]
Comparing election outcomes to our forecast and to the previous election
by Andrew Gelman and Elliott Morris Now that we have almost all the votes from almost all the states, we can step back and answer two questions: 1. How far off were our predictions? 2. How did Joe Biden’s performance compare to Hillary Clinton’s four years earlier? How far off were our predictions? Here’s what […]
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 college
To continue our post-voting, pre-vote-counting assessment (see also here and here), I want to separate two issues which can get conflated:
How the election might have looked in a world without polls
On the radio this morning it was all about how Biden’s in the lead but Trump outperformed the polls just about everywhere. What if there had been no trial-heat polls? Then maybe the reporting would be how Biden outperformed Clinton almost everywhere, but given all the problems with the economy it’s surprising Trump kept it […]
Post-election post
A favorite demonstration in statistics classes is to show a coin and ask what is the probability it comes up heads when flipped. Students will correctly reply 1/2. You then flip the coin high into the air, catch it, slap it on your wrist, look at it, and cover it up again with your hand. […]
Why it can be rational to vote
I think I can best do my civic duty by running this one every Election Day, just like Art Buchwald on Thanksgiving. . . .
Sh*ttin brix in the tail…
After my conversation with Andrew yesterday about The Economist election forecasting model I got curious about how G. Elliot, Merlin and Andrew want their prediction to be assessed given the menu of strange contingencies we have in front of us. I checked Betfair rules for some guidance: This market will be settled according to the candidate […]
As a forecaster, how important is it to “have a few elections under your belt”?
Kevin Lewis pointed me to this comment from Nate Silver on a recent post: Having a few elections under your belt helps a *lot*. No matter how much you test things in the lab, there are some things you’re going to learn only by seeing how your forecast reacts to real data in real time. […]
So, what’s with that claim that Biden has a 96% chance of winning? (some thoughts with Josh Miller)
As indicated above, our model gives Joe Biden a 99+% chance of receiving more votes than Donald Trump and a 96% chance of winning in the electoral college. Michael Wiebe wrote in to ask: Your Economist model currently says that Biden has a 96% chance of winning the electoral college. How should we think about […]
Merlin and me talk on the Bayesian podcast about forecasting the election
Alex Androrra interviewed us, and I guess it makes sense to post the link before the election is over. A couple months ago, Alex interviewed Jennifer, Aki, and me to talk about our book, Regression and Other Stories. I can’t figure out how to directly link to that; you’ll have to follow the above link, […]