Given expectations, our poll is a decent result for Democrats. But remember: It’s just one poll, and we talked to only 495 people. Each candidate’s total could easily be five points different if we polled everyone in the district. And having a small sample is only one possible source of error.

Can Democrats flip Staten Island? We made 25207 calls, and 495 people spoke to us.

This survey was conducted by The New York Times Upshot and Siena College.

Hey, I’m Alex Burns, a politics correspondent for The Times. I’ll give you the latest reporting and intel on the midterms and take your questions from the campaign trail.

It’s generally best to look at a single poll in the context of other polls:

Mr. Rose has worked as chief of staff at a health care nonprofit and as an assistant to the former Brooklyn district attorney. He is campaigning on issues like universal health care and improving the district’s infrastructure. He is also trying to reach out to centrist voters in the district, even running an ad criticizing Mayor Bill de Blasio for neglecting Staten Island.

Mr. Donovan is the only Republican member of New York City’s congressional delegation, and was the only one to support President Trump’s 2017 travel ban on people from several predominantly Muslim countries. He stressed his ties to the president while facing a primary challenge from the right, but now is seeking to appeal to a broader spectrum of voters.

This district is more white and suburban than the rest of New York City, and far more conservative. It went for President Trump in 2016, but is now seen as a battleground, attracting national attention, and national money.

is the current representative and a former district attorney. He voted against both the health care and tax overhauls. 50% favorable rating; 31% unfavorable; 19% don’t know

is a former Army platoon leader who served in Afghanistan. 47% favorable rating; 23% unfavorable; 30% don’t know

Each dot shows one of the 25207 calls we made.

If sampling error were the only type of error in a poll, we would expect candidates who trail by three points in a poll of 495 people to win about two out of every nine races. But this probably understates the total error by a factor of two .

One reason we’re doing these surveys live is so you can see the uncertainty for yourself.

As we reach more people, our poll will become more stable and the margin of sampling error will shrink. The changes in the timeline below reflect that sampling error, not real changes in the race.

Our turnout model There’s a big question on top of the standard margin of error in a poll: Who is going to vote? It’s a particularly challenging question this year, since special elections have shown Democrats voting in large numbers. To estimate the likely electorate, we combine what people say about how likely they are to vote with information about how often they have voted in the past. In previous races, this approach has been more accurate than simply taking people at their word. But there are many other ways to do it. Assumptions about who is going to vote may be particularly important in this race. Our poll under different turnout scenarios Who will vote? Est. turnout Our poll result The types of people who voted in 2014 120k Donovan +8 People whose voting history suggests they will vote, regardless of what they say 155k Donovan +2 Our estimate 156k Donovan +3 People who say they will vote, adjusted for past levels of truthfulness 171k Donovan +2 The types of people who voted in 2016 241k Donovan +6 People who say they are almost certain to vote, and no one else 260k Donovan +13 Every active registered voter 413k Rose +2

The types of people we reached Even if we got turnout exactly right, the margin of error wouldn’t capture all of the error in a poll. The simplest version assumes we have a perfect random sample of the voting population. We do not. People who respond to surveys are almost always too old, too white, too educated and too politically engaged to accurately represent everyone. How successful we were in reaching different kinds of voters Called Inter-

viewed Success

rate Our

respon­ses Goal 18 to 29 2 8 0 6 4 7 1 in 60 9% 10% 30 to 64 1 3 8 5 4 2 9 9 1 in 46 60% 58% 65 and older 5 3 8 4 1 4 9 1 in 36 30% 32% Male 9 4 5 0 2 4 7 1 in 38 50% 46% Female 1 2 6 0 2 2 4 8 1 in 51 50% 54% White 1 2 3 1 3 3 0 9 1 in 40 62% 58% Nonwhite 7 4 0 9 1 3 4 1 in 55 27% 31% Cell 1 3 3 1 5 2 4 5 1 in 54 49% — Landline 8 7 3 7 2 5 0 1 in 35 51% — Pollsters compensate by giving more weight to respondents from under-represented groups. Here, we’re weighting by age, party registration, gender, likelihood of voting, race, education and region, mainly using data from voting records files compiled by L2, a nonpartisan voter file vendor. But weighting works only if you weight by the right categories and you know what the composition of the electorate will be. In 2016, many pollsters didn’t weight by education and overestimated Hillary Clinton’s standing as a result. Here are other common ways to weight a poll: Our poll under different weighting schemes Our poll result Weight using census data instead of voting records, like most public polls Rose +1 Don’t weight by education, like many polls in 2016 Donovan +2 Don’t weight by party registration, like most public polls Donovan +2 Our estimate Donovan +3