Given expectations, our poll is a good result for Republicans. It’s just one poll, though.

Can a supporter of single-payer win a Republican-leaning district? We made 22368 calls, and 512 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:

With the election nearing, both candidates have sought to claim the middle. At a forum in September, they found common ground in the need for overhauling immigration and addressing climate change.

Mr. Bacon, a retired Air Force one-star general, was elected in 2016. He emphasized his military experience to defeat the one-term Democratic incumbent. In this election season he has raised about twice as much as Ms. Eastman.

She says she would support replacing Nancy Pelosi as House speaker in 2019.

Ms. Eastman, the Democratic challenger, won a surprise primary victory over an establishment candidate, the man who had held the seat from 2014 to 2016. Her focus was on single-payer health care, an increase in the minimum wage and fewer abortion restrictions.

This relatively urban district is the most liberal-leaning part of a strongly Republican state and where Democrats see their best opportunity in Nebraska. It went for the Republican candidate in the past two presidential elections but chose Barack Obama in 2008.

But sampling error is not the only type of error in a poll.

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. Our poll under different turnout scenarios Who will vote? Est. turnout Our poll result The types of people who voted in 2014 182k Bacon +11 People who say they are almost certain to vote, and no one else 204k Bacon +9 Our estimate 213k Bacon +9 People whose voting history suggests they will vote, regardless of what they say 216k Bacon +9 People who say they will vote, adjusted for past levels of truthfulness 223k Bacon +9 The types of people who voted in 2016 264k Bacon +17 Every active registered voter 357k Bacon +10 Just because one candidate leads in all of these different turnout scenarios doesn’t mean much by itself. They don’t represent the full range of possible turnout scenarios, let alone the full range of possible election results.

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 1 3 6 0 5 4 1 in 25 11% 11% 30 to 64 1 0 5 9 7 3 1 3 1 in 34 61% 61% 65 and older 3 7 5 1 1 4 5 1 in 26 28% 28% Male 6 4 0 7 2 2 4 1 in 29 44% 47% Female 9 3 0 4 2 8 8 1 in 32 56% 53% White 1 2 4 3 2 3 9 5 1 in 31 77% 78% Nonwhite 1 6 3 2 4 6 1 in 35 9% 11% Cell 1 2 5 0 0 3 9 1 1 in 32 76% — Landline 3 2 1 1 1 2 1 1 in 27 24% — 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 Bacon +4 Don’t weight by party registration, like most public polls Bacon +5 Don’t weight by education, like many polls in 2016 Bacon +9 Our estimate Bacon +9 Just because one candidate leads in all of these different weighting scenarios doesn’t mean much by itself. They don’t represent the full range of possible weighting scenarios, let alone the full range of possible election results.