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.

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:

Ms. Long has stressed her outsider, unpolitician-like credentials (she includes a recipe for rhubarb jam on her campaign website) and her humble background (her father had a small store where family members worked from a young age). She is also emphasizing health care as an issue.

But the results of Washington’s top-two primary and of private polling indicate that this has emerged as a real race.

This contest was not expected to be particularly competitive at the beginning of the cycle. Ms. Herrera Beutler has run well here since winning the seat, and she has broken with her party on some issues, including the health care overhaul.

This district includes Portland, Ore. suburbs, including Vancouver, Wash., and rural areas in southwestern Washington. The small-town, western part of the district was once reliably Democratic, but it has shifted toward Republicans like other blue-collar parts of the country.

is a political science professor who has never run for office before. 38% favorable rating; 22% unfavorable; 40% 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 225k Herrera Beutler +6 Our estimate 257k Herrera Beutler +7 People whose voting history suggests they will vote, regardless of what they say 258k Herrera Beutler +7 People who say they are almost certain to vote, and no one else 268k Herrera Beutler +3 People who say they will vote, adjusted for past levels of truthfulness 279k Herrera Beutler +9 The types of people who voted in 2016 303k Herrera Beutler +15 Every active registered voter 444k Herrera Beutler +12 In these scenarios, higher turnout tends to be better for Republicans. 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 8 8 2 3 0 1 in 96 6% 8% 30 to 64 1 3 4 2 9 2 5 8 1 in 52 52% 55% 65 and older 5 0 6 9 2 0 9 1 in 24 42% 36% Male 9 7 2 3 2 4 6 1 in 40 49% 48% Female 1 1 6 5 9 2 5 1 1 in 46 51% 52% White 1 7 5 0 3 4 4 1 1 in 40 89% 84% Nonwhite 1 8 0 0 2 4 1 in 75 5% 7% Cell 1 1 9 5 4 2 5 8 1 in 46 52% — Landline 9 4 2 8 2 3 9 1 in 39 48% — 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 Don’t weight by education, like many polls in 2016 Herrera Beutler +6 Our estimate Herrera Beutler +7 Don’t weight by primary vote, like most public polls Herrera Beutler +7 Weight using census data instead of voting records, like most public polls Herrera Beutler +8 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.