Everyone remembers where they were. On the night of November 8, 2016, the collective jaw of the American media dropped. If you were watching the tickers, you saw a "blue wall" crumble in real-time. But for months leading up to that night, the data told a completely different story. So, what were the polls like in 2016? Honestly, they were a mix of precision and catastrophic blindness.
People often say the polls were "wrong." That’s a bit of a shortcut. In reality, the national polls were actually pretty decent—they predicted Hillary Clinton would win the popular vote by about 3%, and she won it by 2.1%. Not a huge miss. The real disaster lived in the state-level data. Places like Pennsylvania, Michigan, and Wisconsin were supposed to be safe. They weren't.
The "Shy Trump" Theory and Other Myths
One of the biggest things people talk about when discussing what the polls were like in 2016 is the "Shy Trump Supporter" effect. The idea was that people were embarrassed to tell a live caller they were voting for Donald Trump. It's a catchy theory. However, most post-election post-mortems, including a massive one by the American Association for Public Opinion Research (AAPOR), found little evidence for this.
If people were lying to pollsters, we would have seen a huge discrepancy between live-phone polls and anonymous online surveys. We didn't. The error was more mechanical than psychological. It was about who the pollsters were actually reaching on the phone and, more importantly, how they weighted those responses.
The Great Educational Divide
This is the big one. If you want to understand why the 2016 polls missed the mark, you have to look at "educational weighting." Historically, education wasn't a massive predictor of how someone would vote. A white voter with a college degree and a white voter without one often moved in the same direction.
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2016 changed that.
The polling industry didn't react fast enough. Many state-level polls didn't adjust their samples to make sure they had the right proportion of non-college-educated voters. Since non-college-educated white voters turned out in record numbers for Trump, and they were underrepresented in the polls, the results were skewed. It’s a classic "garbage in, garbage out" scenario. If your sample doesn't look like the people who actually show up at the booth, your numbers are essentially fiction.
Late Undecideds and the Comey Letter
Timing is everything in politics. In a typical election year, undecided voters usually split somewhat evenly between the two main candidates. Not in 2016. According to Nate Silver of FiveThirtyEight, there was an unusually high number of undecided voters—around 13 to 15 percent—just a week before the election.
Then came the "Comey Letter."
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On October 28, FBI Director James Comey announced the FBI was reopening the investigation into Clinton’s emails. In the final days, those undecided voters broke heavily for Trump, especially in the Rust Belt. Most pollsters had already stopped "fielding" (calling people) by the time this shift happened. They were capturing a snapshot of a race that had already changed.
What the Polls Were Like in 2016: State vs. National
Let's get into the weeds of the "Blue Wall." Michigan, Wisconsin, and Pennsylvania. These states were the graveyard of the Clinton campaign.
In Wisconsin, for example, there wasn't a single high-quality non-partisan poll that showed Trump leading in the final month. Not one. This created a sense of false security. The Clinton campaign didn't even visit Wisconsin during the general election. They trusted the data. But the data was missing the surge of rural, white, working-class voters who hadn't participated in years.
Why state polls failed while national polls didn't:
- Lower Budgets: State polls often have less money, meaning smaller sample sizes and cheaper methodology.
- Frequency: National polls happen daily; state polls might only happen once every few weeks.
- Weighting: As mentioned, many state polls failed to weight by education, whereas some national polls did.
The Margin of Error Problem
We often treat a poll result like a final score. If a poll says "Clinton 46, Trump 44," we think she's winning. But every poll has a Margin of Error (MoE), usually around +/- 3 or 4 points.
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In 2016, Trump was frequently within the margin of error in the swing states. If a candidate is down by 2 points with a 4-point margin of error, they are technically in a dead heat. The media, and the public, largely ignored the "uncertainty" and focused on the "lead." We saw a 70% or 90% "chance" of winning and assumed that meant a 100% certainty.
A New Era of Skepticism
Since 2016, the polling industry has gone through a mid-life crisis. They've started weighting by education. They've experimented with "text-to-web" polling and more sophisticated online modeling. But the ghost of 2016 still haunts every election cycle. It taught us that "likely voter" models are just guesses. If a candidate can bring out people who don't usually vote, the model breaks.
How to Read Polls Today Without Getting Fooled
Don't just look at the headline number. It’s tempting, but it’s lazy. To truly understand where a race stands, you have to look under the hood.
- Check the "Unset" Voters: If "Undecided" or "Third Party" is higher than 5%, expect volatility. The race isn't settled.
- Look for Educational Weighting: Reliable pollsters now explicitly state if they weighted for education. If they didn't, discard the poll.
- Find the Trend, Not the Outlier: One poll showing a 10-point lead is irrelevant. Look at the polling average (like RealClearPolitics or FiveThirtyEight).
- Ignore the "Win Probability": A 20% chance of winning is the same as a 1-in-5 shot. If you played Russian Roulette with a 5-chamber revolver, you wouldn't feel "safe." Those are the same odds Trump had in many models.
The reality of what the polls were like in 2016 is that they were a snapshot of a country undergoing a massive demographic and political realignment that the tools of the time weren't calibrated to see. The numbers weren't "fake"—they were just incomplete. Understanding that distinction is the only way to make sense of the data we see today.