New district lines and a new challenge for a Democratic incumbent. We made 20465 calls, and 506 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:

In a campaign ad, Mr. Cartwright said Mr. Chrin would help cut Social Security. Mr. Chrin countered with an ad featuring his mother in which he said he would never do such a thing.

Mr. Chrin has criticized Mr. Cartwright for being “too liberal and out of touch” and for supporting sanctuary cities, part of a recent line of attack by G.O.P candidates.

Mr. Cartwright won by eight percentage points in the 17th District in 2016, even as Donald Trump won it by 10 points, consistent with a shift toward Republicans in old industrial areas of the Northeast and Midwest with traditionally Democratic roots.

The new Eighth District took much of the territory of the old 17th, essentially swapping out some conservative territory for some other conservative territory, leaving the partisan balance roughly the same. The district includes Scranton and Wilkes-Barre and is a mix of suburban and rural communities, mostly white and middle class.

is the U.S. representative for Pennsylvania's 17th District, first elected in 2012. 53% favorable rating; 29% unfavorable; 19% 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 The types of people who voted in 2014 166k Cartwright +9 People whose voting history suggests they will vote, regardless of what they say 214k Cartwright +12 Our estimate 214k Cartwright +13 People who say they will vote, adjusted for past levels of truthfulness 231k Cartwright +13 People who say they are almost certain to vote, and no one else 248k Cartwright +20 The types of people who voted in 2016 291k Cartwright +9 Every active registered voter 409k Cartwright +16 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 7 1 4 0 1 in 34 8% 8% 30 to 64 1 2 0 4 9 2 6 8 1 in 45 53% 57% 65 and older 5 0 1 1 1 9 8 1 in 25 39% 35% Male 8 3 6 6 2 5 4 1 in 33 50% 49% Female 1 0 0 7 0 2 5 2 1 in 40 50% 51% White 1 3 9 0 6 3 8 9 1 in 36 77% 76% Nonwhite 1 8 9 7 4 3 1 in 44 8% 10% Cell 1 0 5 9 3 2 6 8 1 in 40 53% — Landline 7 8 4 3 2 3 8 1 in 33 47% — 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 Don’t weight by education, like many polls in 2016 Cartwright +15 Our estimate Cartwright +13 Don’t weight by party registration, like most public polls Cartwright +10 Weight using census data instead of voting records, like most public polls Cartwright +7 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.