Our poll is a good result for Democrats. It’s just one poll, though.

Could concern over a trade war cost the Republican incumbent? We made 17716 calls, and 502 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:

Ms. Finkenauer, 29, who was elected to the Iowa Legislature in 2014, has focused on pro-labor policies and on protecting women’s health and reproductive rights. She and Alexandria Ocasio-Cortez have a chance to be the first women under 30 elected to Congress.

Iowa relies on global trade, and a trade war could be a problem for Mr. Blum . “I’m not on the ledge ready to jump out the window concerning trade, but I do have the window open a little bit,” he said.

Mr. Blum, a software entrepreneur from Dubuque, has emphasized his bootstrap origins: His mother cleaned houses for a living, and his father left school in 10th grade. He identifies as a Tea Party Republican, and is considered a member of the House Freedom Caucus .

This district backed Barack Obama twice by double digits, then Donald J. Trump won it by four points.

is the incumbent, first elected in 2014, and a former businessman. 35% favorable rating; 53% unfavorable; 12% don’t know

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 People who say they are almost certain to vote, and no one else 245k Finkenauer +25 The types of people who voted in 2014 286k Finkenauer +11 Our estimate 306k Finkenauer +14 People whose voting history suggests they will vote, regardless of what they say 314k Finkenauer +13 People who say they will vote, adjusted for past levels of truthfulness 318k Finkenauer +15 The types of people who voted in 2016 367k Finkenauer +12 Every active registered voter 472k Finkenauer +14 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 7 5 5 4 2 1 in 42 8% 9% 30 to 64 9 6 9 4 2 8 7 1 in 34 57% 57% 65 and older 3 7 9 2 1 7 3 1 in 22 34% 34% Male 6 7 0 6 2 0 0 1 in 34 40% 47% Female 8 5 3 6 3 0 2 1 in 28 60% 53% White 1 2 5 9 8 4 1 9 1 in 30 83% 83% Nonwhite 7 2 7 2 1 1 in 35 4% 5% Cell 9 9 7 2 2 8 6 1 in 35 57% — Landline 5 2 7 0 2 1 6 1 in 24 43% — 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 Finkenauer +19 Don’t weight by party registration, like most public polls Finkenauer +18 Don’t weight by education, like many polls in 2016 Finkenauer +15 Our estimate Finkenauer +14 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.