Given expectations, our poll is a good result for Democrats. But remember: It’s just one poll, and we talked to only 428 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 a young Democratic challenger win on health care? We made 23551 calls, and 428 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. Underwood, an African-American candidate in an overwhelmingly white district, said , “This is my home, and the idea that I might not be a good fit is an idea I never gave a lot of consideration to.”

Ms. Underwood, a 31-year-old Naperville native, embraces the progressive label and said Mr. Hultgren’s vote to repeal the Affordable Care Act was her catalyst to run. She is putting health care at the center of her campaign.

Mr. Hultgren hasn’t had a serious challenge since 2010 and says his conservative record suits the district. He voted for the Republican tax bill and is promoting the strong economy.

This district, in the exurbs west of Chicago, has long had a Republican bent. Dennis Hastert, the longest-serving Republican Speaker of the House in history, represented it from 1987 to 2007.

is a nurse and former senior adviser at the Department of Health and Human Services under President Obama. 50% favorable rating; 29% unfavorable; 21% don’t know

Each dot shows one of the 23551 calls we made.

If sampling error were the only type of error in a poll, we would expect candidates who trail by five points in a poll of 428 people to win about one out of every seven 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 235k Underwood +2 People whose voting history suggests they will vote, regardless of what they say 278k Underwood +4 Our estimate 281k Underwood +5 People who say they will vote, adjusted for past levels of truthfulness 296k Underwood +7 People who say they are almost certain to vote, and no one else 326k Underwood +18 The types of people who voted in 2016 329k Hultgren +2 Every active registered voter 470k Underwood +6

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 7 4 4 1 in 38 10% 9% 30 to 64 1 4 7 8 1 2 6 7 1 in 55 62% 64% 65 and older 4 6 4 4 1 1 4 1 in 41 27% 27% Male 9 0 6 8 1 9 9 1 in 46 46% 48% Female 1 2 0 5 4 2 2 9 1 in 53 54% 52% White 1 6 2 4 6 3 3 1 1 in 49 77% 77% Nonwhite 2 3 1 8 4 2 1 in 55 10% 11% Cell 1 3 0 2 9 2 8 4 1 in 46 66% — Landline 8 0 9 3 1 4 4 1 in 56 34% — Pollsters compensate by giving more weight to respondents from under-represented groups. Here, we’re weighting by age, primary vote, 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 Don’t weight by primary vote, like most public polls Underwood +8 Weight using census data instead of voting records, like most public polls Underwood +8 Don’t weight by education, like many polls in 2016 Underwood +6 Our estimate Underwood +5 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.