Our poll is a decent result for Republicans. But remember: It’s just one poll, and we talked to only 504 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.

A Republican-held seat Democrats believe they can flip. We made 36667 calls, and 504 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. Knight was first elected in 2014 and has deep roots in the area: He was a Los Angeles police officer, a Palmdale City Council member and a state lawmaker. He joined centrist colleagues in a failed effort to force a vote on immigration measures this spring, but critics say he has not done enough to distinguish himself from the Trump administration on immigration.

Ms. Hill has raised far more money than Mr. Knight, as of the most recent reports . She’s among the candidates that Barack Obama was supporting in his recent appearance in Orange County.

This district, just north of Los Angeles, is one of the Democrats’ biggest targets. Hillary Clinton won here in 2016, but it has been represented by a Republican since 1993. The district is now about 40 percent Hispanic, and registered Democrats outnumber Republicans.

If sampling error were the only type of error in a poll, we would expect candidates who trail by four points in a poll of 504 people to win about one out of every six 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. Our poll under different turnout scenarios Who will vote? Est. turnout Our poll result The types of people who voted in 2014 148k Knight +9 People whose voting history suggests they will vote, regardless of what they say 187k Knight +5 Our estimate 188k Knight +4 People who say they will vote, adjusted for past levels of truthfulness 200k Knight +5 People who say they are almost certain to vote, and no one else 239k Knight +3 The types of people who voted in 2016 258k Knight +2 Every active registered voter 367k Knight +5 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 2 9 0 2 5 8 1 in 50 12% 12% 30 to 64 1 8 1 4 3 3 1 0 1 in 59 62% 61% 65 and older 5 8 5 2 1 3 6 1 in 43 27% 27% Male 1 0 9 0 3 2 5 9 1 in 42 51% 47% Female 1 6 0 0 7 2 4 5 1 in 65 49% 53% White 1 4 2 8 6 2 9 7 1 in 48 59% 59% Nonwhite 1 0 5 4 6 1 6 8 1 in 63 33% 33% Cell 1 9 9 4 8 3 6 3 1 in 55 72% — Landline 6 9 6 2 1 4 1 1 in 49 28% — 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 Knight +3 Weight using census data instead of voting records, like most public polls Knight +4 Our estimate Knight +4 Don’t weight by party registration, like most public polls Knight +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.