Republican presidential candidate Donald Trump in Cleveland, Ohio on October 22, 2016 and US Democratic presidential nominee Hillary Clinton in Manchester, New Hampshire, on November 6, 2016.

Jay LaPrete and Brendan Smialowski/Getty Images

The 2016 presidential election between Donald Trump and Hillary Clinton taught us that forecasting is hard, survey nonresponse is a thing, and many other important lessons.

What does the November election shocker mean for the various theories and shibboleths floating around political science and political journalism? With the help of my colleague Bob Erikson, I came up with 19 lessons learned.

1. The party doesn’t decide. We can start with the primaries, which destroyed the “party decides” theory of Marty Cohen, David Karol, Hans Noel, and John Zaller, who wrote in 2008 that “unelected insiders in both major parties have effectively selected candidates long before citizens reached the ballot box.” You can’t blame the authors of a book on political history—its subtitle is Presidential Nominations Before and After Reform—for failing to predict the future. But it does seem that the prestige of the “party decides” model was one reason that Nate Silver, Nate Cohn, Jonathan Chait, and a bunch of other pundits not named Nate or Jonathan were so quick to dismiss Donald Trump’s chances of winning in the Republican primaries.

Indeed, I myself was tempted to dismiss Trump’s chances during primary season, but then I read that article I’d written in 2011 explaining why primary elections are so difficult to predict (multiple candidates, no party cues or major ideological distinctions between them, unequal resources, unique contests, and rapidly changing circumstances), and I decided to be careful with any predictions.

2. That trick of forecasting elections using voter predictions rather than voter intentions? Doesn’t work. Economists David Rothschild and Justin Wolfers have argued that the best way to predict the election is not to ask people whom they’ll vote for but rather ask whom they think will win. Their claim was that when you ask people whom they think will win, survey respondents will be informally tallying their social networks, hence their responses will contain valuable information for forecasting. When this idea was hyped back in 2012, I was skeptical, taking the position that respondents would be doing little more than processing what they’d seen in the news media, and I remain skeptical, following a 2016 election that was a surprise to most.

3. Survey nonresponse is a thing. It’s harder and harder to reach a representative sample of voters, and it’s been argued that much of the swing in the polls is attributable not to people changing their vote intention but to changes in who responds or doesn’t respond. In short, when there is good news about a candidate, his or her supporters are more likely to respond to polls. Doug Rivers, David Rothschild, Sharad Goel, and I floated this theory following some analysis of opinion polls from 2012, and it seems to have held up well during the recent campaign season.

The only hitch here is that the differential nonresponse story explains variation in the polls but not the level or average shift. The final polls were off by about 2 percentage points, suggesting that, even at the end, Trump supporters were responding at a lower rate than Clinton supporters.

4. The election outcome was consistent with “the fundamentals.” Various models predict the election outcome not using the polls, instead using the national economy (as measured, for example, in inflation-adjusted personal income growth during the year or two preceding the election) and various political factors. In 2016 the economy was growing slowly but not booming (a mixed signal for the voters), the incumbent party was going for a third term in office (traditionally a minus, as voters tend to support alternation), and the Republicans controlled both houses of Congress (a slight benefit for the Democrats in presidential voting, for that minority of voters who prefer party balancing), and, on the left-right scale, both candidates were political centrists relative to other candidates from their parties. This information can be combined in different ways: Running a version of the model constructed by the political scientist Doug Hibbs, I gave Hillary Clinton a forecast of 52 percent of the two-party vote. Fitting a similar model but with slightly different parameters, political scientist Drew Linzer gave Clinton 49 percent. In October the political science journal PS published several articles on forecasting the election, including one from Bob Erikson and Chris Wlezien that concluded, “The possibility of greater campaign effects than we typically observe should constrain our confidence in the predictions presented here.”

All these fundamentals-based models have uncertainties on the order of 3 percentage points, so what they really predicted is that the election would not be a landslide. The actual outcome was consistent with these predictions. That said, a wide range of outcomes—anything from 55-45 to 45-55—would’ve jibed with some of these forecasts. And the nonblowout can also be explained by countervailing factors: Perhaps Trump was so unpopular that anyone but Clinton would’ve destroyed him in the general election and vice versa. That seems doubtful, but who knows.

5. Polarization is real. Democrats vote for Democrats, Republicans vote for Republicans. It’s always been thus—what would the party labels mean, otherwise?—but cross-party voting keeps declining, and members of the out-party hold the president in lower and lower esteem. Consider, for example, Donald Trump’s criticism of Barack Obama during the presidential debates. Obama is popular so this might seem to have been a mistake to stand against him—but Obama is deeply unpopular among Republicans, especially those Republicans who are likely to vote.

A corollary of polarization is that, if there aren’t many people in the middle to be persuaded, it makes sense for candidates to focus on firing up their bases, and this is a key part of the story of the success of the Trump campaign. You can bet that activists of both parties will have learned this lesson when 2020 comes along.

6. Demography is not destiny. We’d been hearing a lot about how the Republican Party, tied to a declining base of elderly white supporters, needs to reassess. In October, a tracking poll from Latino Decisions had Donald Trump with 16 percent support among Latinos, versus 74 percent for Hillary Clinton. The group projected Latinos to go for Clinton, 82 percent to 15 percent. According to exit polls, the Latino vote ended up dividing 66-28, a clear Clinton lead but nothing like the forecast from Latino Decisions—a forecast that should’ve been suspect, given that it contradicted the organization’s own polls! Longer term, it may well be that the Republican Party needs to change with the times, but destiny hasn’t arrived yet.

7. Public opinion does not follow elite opinion. Perhaps the most disturbing theoretical failure of political science is the general idea that voters simply follow elite opinion. This worked in 1964 to destroy Goldwater, for instance. Or so the story goes. The implication is that voters had to be told Goldwater was scary. They could not figure it out for themselves.

In 2016, Trump was opposed vigorously as dangerous, incompetent, xenophobic, tyrannical, and unhinged, by almost everybody in elite circles: Most of his Republican primary opponents at one time or another, a large number of conservative intellectuals, former Republican candidates Mitt Romney and John McCain, the various Bushes, the media, almost all newspaper editorialists including those at papers that were reliable Republican supporters, all Democrats, about 10 Republican senators, and even some pundits on Fox News. Further, Trump’s habit of breaking the standard niceties of politics was there…