Our poll result is about what was expected. But remember: It’s just one poll, and we talked to only 501 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 win back rural Obama-Trump voters? We made 17240 calls, and 501 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. Golden once worked for a Republican senator, Susan Collins, and is assistant leader in the Maine House.

Mr. Golden, a tattooed former Marine who served in Iraq and Afghanistan, has been running left on economic issues like universal health care and prekindergarten, an expansion of veterans and Social Security benefits, and stronger labor laws.

Mr. Poliquin, New England’s only Republican House member, voted for the Republican tax cut plan and for the repeal of the Affordable Care Act. He refused to endorse Donald J. Trump in 2016.

(We polled this district from Sept. 12 to 14. That poll showed a slight edge for Mr. Poliquin.)

This geographically large, mostly rural district encompasses the entire northern part of the state as well as the coast east of Rockland. No incumbent here has lost a race since 1916, according to Ballotpedia.

is the current representative and a former state treasurer with a background in finance. 44% favorable rating; 45% unfavorable; 10% don’t know

is a representative in the Maine House and a Marine Corps veteran. 46% favorable rating; 39% unfavorable; 15% don’t know

Each dot shows one of the 17240 calls we made.

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. 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 People who say they are almost certain to vote, and no one else 255k Poliquin +5 The types of people who voted in 2014 287k Poliquin +1 People whose voting history suggests they will vote, regardless of what they say 303k Golden +1 Our estimate 303k Even The types of people who voted in 2016 317k Poliquin +2 People who say they will vote, adjusted for past levels of truthfulness 329k Even Every active registered voter 429k Golden +3 In these scenarios, higher turnout tends to be better for Democrats.

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 6 7 1 1 4 1 in 119 3% 8% 30 to 64 9 5 1 1 2 7 8 1 in 34 55% 58% 65 and older 2 8 1 1 2 0 8 1 in 14 42% 34% Male 6 6 1 0 2 5 5 1 in 26 51% 48% Female 7 3 8 8 2 4 6 1 in 30 49% 52% White 1 2 7 0 7 4 5 9 1 in 28 92% 91% Nonwhite 3 9 0 1 2 1 in 33 2% 3% Cell 7 6 1 0 1 9 2 1 in 40 38% — Landline 6 3 8 8 3 0 9 1 in 21 62% — 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. Even after weighting, our poll does not have as many of some types of people as we would like. Here are other common ways to weight a poll: Our poll under different weighting schemes Our poll result Don’t weight by education, like many polls in 2016 Even Our estimate Even Weight using census data instead of voting records, like most public polls Poliquin +1 Don’t weight by party registration, like most public polls Poliquin +2