But remember: It’s just one poll, and we talked to only 498 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.

Did redistricting make a conservative district a competitive one? We made 12157 calls, and 498 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:

The Democratic Congressional Campaign Committee thinks enough of Mr. Scott’s chances that it recently dropped $260,000 into the race.

Mr. Scott recently said, “People are sick of the divisiveness, they're sick of the extreme partisanship, they're sick of groups like the Freedom Caucus.”

Like Mr. Scott, Mr. Perry is an Iraq war veteran. About 40 percent of the district is new to him because of redistricting. He’s considered a member of the conservative Freedom Caucus, and his voting record is among the most conservative in the House.

Perhaps no Republican was more surprised by the new redistricting map than Mr. Perry, an incumbent from safely conservative York County in the Fourth District. His old district voted for Mr. Trump by 21 points; the new 10th voted for the president by nine points.

is a House incumbent, first elected in 2012 in the state’s Fourth District, and a former state legislator. 44% favorable rating; 34% unfavorable; 22% don’t know

is a first-time candidate and a Lutheran pastor. He served with the Army in Iraq. 44% favorable rating; 29% unfavorable; 27% don’t know

Each dot shows one of the 12157 calls we made.

If sampling error were the only type of error in a poll, we would expect candidates who trail by two points in a poll of 498 people to win about three out of every eight 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 221k Perry +7 People whose voting history suggests they will vote, regardless of what they say 254k Perry +2 Our estimate 254k Perry +2 People who say they will vote, adjusted for past levels of truthfulness 274k Perry +3 People who say they are almost certain to vote, and no one else 283k Even The types of people who voted in 2016 327k Perry +1 Every active registered voter 435k Perry +1 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 8 1 4 4 3 1 in 19 9% 8% 30 to 64 7 5 5 3 2 7 5 1 in 27 55% 60% 65 and older 3 0 6 0 1 8 0 1 in 17 36% 32% Male 4 8 7 5 2 3 7 1 in 21 48% 47% Female 6 5 5 6 2 6 1 1 in 25 52% 53% White 8 7 6 7 3 7 2 1 in 24 75% 78% Nonwhite 1 3 6 4 6 0 1 in 23 12% 11% Cell 6 6 6 1 2 7 4 1 in 24 55% — Landline 4 7 7 0 2 2 4 1 in 21 45% — 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 Even Our estimate Perry +2 Don’t weight by party registration, like most public polls Perry +3 Weight using census data instead of voting records, like most public polls Perry +4 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.