Our poll result is about what was expected. But remember: It’s just one poll, and we talked to only 508 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 battle between Navy veterans in a coastal district. We made 34134 calls, and 508 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:

Allegations surfaced last month that members of Mr. Taylor’s staff forged signatures to help get an independent candidate on the ballot, presumably with the aim of siphoning votes from Ms. Luria. A special prosecutor has been appointed. Mr. Taylor conceded that he was “aware of the effort to get signatures,” but “what I was not aware of at all was any wrongdoing by anybody at the time.”

Ms. Luria is among a crop of Democratic women with a military or national security background who are making a first venture into politics.

(We polled this district from Sept. 26 to Oct. 1. That showed the incumbent with a modest lead.)

This district includes Virginia Beach, the Norfolk Naval Base and the Virginia portion of Delmarva. It has been in Republican hands for all but one term of the last two decades. Donald J. Trump carried the district by three points.

is the current representative and a former member of the Navy SEALs. 51% favorable rating; 32% unfavorable; 18% don’t know

Each dot shows one of the 34134 calls we made.

If sampling error were the only type of error in a poll, we would expect candidates who trail by three points in a poll of 508 people to win about one out of every four 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 192k Taylor +6 People whose voting history suggests they will vote, regardless of what they say 207k Taylor +3 Our estimate 207k Taylor +3 People who say they will vote, adjusted for past levels of truthfulness 225k Luria +1 People who say they are almost certain to vote, and no one else 253k Luria +1 The types of people who voted in 2016 299k Even Every active registered voter 431k Luria +6 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 2 8 3 4 3 4 1 in 83 7% 8% 30 to 64 1 5 5 6 6 3 0 5 1 in 51 60% 58% 65 and older 5 6 1 9 1 6 9 1 in 33 33% 33% Male 1 0 9 0 8 2 5 9 1 in 42 51% 46% Female 1 3 1 1 4 2 4 9 1 in 53 49% 54% White 1 6 5 1 8 3 6 4 1 in 45 72% 71% Nonwhite 5 7 1 6 1 0 1 1 in 57 20% 22% Cell 1 5 8 3 5 2 8 4 1 in 56 56% — Landline 8 1 8 7 2 2 4 1 in 37 44% — 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 Weight using census data instead of voting records, like most public polls Taylor +2 Don’t weight by primary vote, like most public polls Taylor +2 Our estimate Taylor +3 Don’t weight by education, like many polls in 2016 Taylor +3 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.