Given expectations, our poll is a good result for Democrats. It’s just one poll, though.

A swing district in the desert Southwest. We made 43136 calls, and 502 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.

Ms. Marquez Peterson, who owns gas stations and convenience stores and is a newcomer to politics, has stressed her business acumen. She lags in fund-raising behind Ms. Kirkpatrick, having raised less than half as much in the most recent reporting cycle.

Ms. Kirkpatrick represented Arizona’s First Congressional District for three terms and has also been a state lawmaker and deputy county attorney. She is highlighting her political experience and her support for the Affordable Care Act and gun control.

This seat is open because the current officeholder, Martha McSally, is running for the Senate. The district, which includes much of Tucson, went for Hillary Clinton in 2016 but chose the Republican in the previous four presidential elections. Partisan registration is about evenly divided.

is a business owner and president of the Tucson Hispanic Chamber of Commerce. 27% favorable rating; 24% unfavorable; 48% don’t know

is a lawyer and a former state and federal lawmaker 40% favorable rating; 38% unfavorable; 22% don’t know

Each dot shows one of the 43136 calls we made.

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 211k Kirkpatrick +7 People who say they are almost certain to vote, and no one else 229k Kirkpatrick +5 Our estimate 257k Kirkpatrick +11 People whose voting history suggests they will vote, regardless of what they say 261k Kirkpatrick +11 People who say they will vote, adjusted for past levels of truthfulness 263k Kirkpatrick +13 The types of people who voted in 2016 285k Kirkpatrick +8 Every active registered voter 394k Kirkpatrick +15 In these scenarios, higher turnout tends to be better for Democrats. 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 1 2 8 1 in 103 6% 8% 30 to 64 1 3 9 8 9 2 7 1 1 in 52 54% 50% 65 and older 7 3 6 7 2 0 2 1 in 36 40% 43% Male 1 0 8 0 4 2 4 0 1 in 45 48% 47% Female 1 3 4 5 9 2 6 2 1 in 51 52% 53% White 1 6 6 8 1 3 7 5 1 in 44 75% 73% Nonwhite 5 5 2 6 7 8 1 in 71 16% 18% Cell 1 6 6 9 5 3 0 4 1 in 55 61% — Landline 7 5 6 8 1 9 8 1 in 38 39% — 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 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. Even after weighting, our poll does not have as many of some types of people as we would like. 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 Kirkpatrick +13 Don’t weight by party registration, like most public polls Kirkpatrick +13 Don’t weight by education, like many polls in 2016 Kirkpatrick +12 Our estimate Kirkpatrick +11 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.