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.

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.

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.

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 1 1 3 4 5 1 1 in 22 10% 9% 30 to 64 1 0 5 7 7 3 0 4 1 in 35 60% 62% 65 and older 3 7 4 5 1 4 8 1 in 25 29% 29% Male 6 5 1 7 2 2 0 1 in 30 44% 47% Female 8 9 4 3 2 8 3 1 in 32 56% 53% White 1 2 1 3 8 4 0 7 1 in 30 81% 79% Nonwhite 1 7 8 3 5 2 1 in 34 10% 11% Cell 1 0 5 1 8 3 6 3 1 in 29 72% — Landline 4 9 4 2 1 4 0 1 in 35 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 Davids +11 Our estimate Davids +9 Weight using census data instead of voting records, like most public polls Davids +9 Don’t weight by party registration, like most public polls Davids +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.