But remember: It’s just one poll, and we talked to only 542 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.

Will Democrats blow a seat that seemed to fall in their laps? We made 33769 calls, and 542 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:

Ms. Shalala, 77, doesn’t speak Spanish, and her long track record brings an equally long list of potential vulnerabilities for Republicans to attack.

About six in 10 percent of registered voters here are Latino, most of them Cuban-American, as is Ms. Salazar. She’s a familiar and trusted face for many in the area; she worked for Telemundo and other stations for three decades.

The retirement of the Republican incumbent, Ileana Ros-Lehtinen, a Cuban-American, in a district Hillary Clinton won by 20 percentage points seemed to suggest an easy victory for Democrats. But it hasn’t worked out that way, with polls showing a tight race .“It shouldn’t even be this close,” a former chairman of the Miami-Dade County Democratic Party told The New York Times. “I know from my Republican friends that they’re kind of bullish. It has me nervous because it would be a devastating loss.”

This traditionally Republican district, which includes wealthy communities like Miami Beach, Key Biscayne and Coral Gables as well as Little Havana in Miami, has been trending Democratic in recent years.

was the health and human services secretary under President Bill Clinton, a president of the University of Miami for 14 years, and a president of the Clinton Foundation. 48% favorable rating; 28% unfavorable; 24% 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. 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 168k Shalala +4 People whose voting history suggests they will vote, regardless of what they say 205k Shalala +7 Our estimate 205k Shalala +7 People who say they will vote, adjusted for past levels of truthfulness 224k Shalala +8 People who say they are almost certain to vote, and no one else 238k Shalala +7 The types of people who voted in 2016 273k Salazar +2 Every active registered voter 387k Shalala +7

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 4 7 5 4 0 1 in 62 7% 10% 30 to 64 1 4 9 3 2 3 4 3 1 in 44 63% 55% 65 and older 6 0 9 7 1 5 9 1 in 38 29% 35% Male 1 0 1 2 4 2 7 4 1 in 37 51% 46% Female 1 3 3 9 8 2 6 8 1 in 50 49% 54% White 4 8 6 9 1 4 8 1 in 33 27% 24% Nonwhite 1 7 6 9 3 3 5 9 1 in 49 66% 71% Cell 1 6 5 1 6 4 2 2 1 in 39 78% — Landline 7 0 0 6 1 2 0 1 in 58 22% — 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 Shalala +10 Our estimate Shalala +7 Weight using census data instead of voting records, like most public polls Shalala +7 Don’t weight by party registration, like most public polls Shalala +6 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.