Our poll result is about what was expected. But remember: It’s just one poll, and we talked to only 497 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.

Republicans are trying to hold a well-educated district that swung 15 points toward Hillary Clinton. We made 53246 calls, and 497 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. Roskam won by 18 points in 2016 and entered the summer with a four-to-one lead in cash on hand, an important advantage in an expensive media market. But Mr. Casten has narrowed the gap considerably .

This district, in the western suburbs of Chicago, has a median income of nearly $100,000.

Illinois’s Sixth is emblematic of a type of district that could decide control of the House: a well-educated suburb that voted for Hillary Clinton but that usually votes Republican and has a strong incumbent against a political newcomer.

is the current representative and a former lawyer. He voted for the tax reform bill and to repeal and replace the Affordable Care Act. 47% favorable rating; 40% unfavorable; 13% don’t know

Each dot shows one of the 53246 calls we made.

If sampling error were the only type of error in a poll, we would expect candidates who trail by one point in a poll of 497 people to win about two out of every five 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 248k Roskam +6 People whose voting history suggests they will vote, regardless of what they say 300k Casten +1 Our estimate 301k Casten +1 People who say they are almost certain to vote, and no one else 311k Casten +11 People who say they will vote, adjusted for past levels of truthfulness 327k Casten +4 The types of people who voted in 2016 341k Casten +3 Every active registered voter 479k Casten +5

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 5 6 7 6 6 1 in 39 13% 10% 30 to 64 1 9 0 9 3 3 1 7 1 in 60 64% 62% 65 and older 6 6 7 5 1 1 2 1 in 60 23% 28% Male 1 2 2 1 0 2 4 3 1 in 50 49% 48% Female 1 6 1 7 0 2 5 4 1 in 64 51% 52% White 2 0 9 4 3 3 5 9 1 in 58 72% 75% Nonwhite 3 8 9 1 6 5 1 in 60 13% 13% Cell 2 0 4 6 2 3 5 1 1 in 58 71% — Landline 7 9 1 8 1 4 6 1 in 54 29% — 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 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 education, like many polls in 2016 Casten +3 Our estimate Casten +1 Don’t weight by primary vote, like most public polls Even Weight using census data instead of voting records, like most public polls Even