Election cycles are flooded with polls, shaping headlines and shaping perceptions of who is “winning” or “losing” the race. But time and time again, we see why electoral polls fail, especially in recent elections like 2016 and 2020, where polling results were dramatically off. This happens because polling relies on a vast number of assumptions—about human behavior, voter turnout, and the accuracy of responses—that simply cannot be fully controlled. These assumptions lead to errors that can skew poll results by as much as 10%.
Understanding why electoral polls fail starts with examining the core assumptions pollsters rely on. Polling involves gathering data from a small sample of the population and using it to predict the outcome of an election. But this process rests on a number of shaky assumptions, each of which introduces the potential for error.
The first and most basic assumption in polling is that the respondent will actually vote. Pollsters ask individuals if they plan to vote, and many will say yes. However, voter turnout is notoriously difficult to predict. Someone might intend to vote but not follow through for any number of reasons—work, illness, bad weather, or simply changing their mind. This introduces an element of uncertainty right from the start.
Pollsters also assume that respondents are being truthful. But in reality, people often lie in polls, especially when discussing controversial candidates. For example, in 2016, many pollsters underestimated Donald Trump’s support because some of his voters were reluctant to admit their choice to pollsters due to social stigma. This “social desirability bias” leads to flawed data and distorted results.
The way questions are framed can significantly affect responses. Pollsters assume they are asking the right questions, but slight differences in wording can lead to vastly different outcomes. For example, asking, “Who do you plan to vote for?” versus “Do you support Candidate A?” might elicit different answers, even if the respondent’s voting intention hasn’t changed.
Even if the right question is asked, there’s no guarantee the respondent will answer it accurately or in the way pollsters expect. People might misunderstand the question, or they might answer based on their feelings at that moment, which may not reflect their true voting intention. This adds another layer of complexity and potential error.
Pollsters try to create a sample that reflects the overall population, but certain groups—like younger voters, minorities, or rural residents—might be underrepresented. If a sample isn’t truly representative of the electorate, the results can be skewed. For instance, a poll that underrepresents young voters might overstate support for more conservative candidates.
Every poll comes with a margin of error, which is typically around ±3%. This means that if a candidate is polling at 47%, their actual support could be anywhere from 44% to 50%. In close races, this margin of error can make a huge difference, turning what appears to be a lead into a statistical tie.
Let’s illustrate this with an analogy. Imagine a big bowl of 1,000 M&Ms—some red, some blue. You know the exact number of red and blue M&Ms in the bowl because you can count them. Now, if you asked 100 people to guess how many red and blue M&Ms are in the bowl, you would eventually start to see the guesses average out around the true number.
This example shows the power of known variables. You can mathematically predict the outcome because you know exactly how many red and blue M&Ms are in the bowl.
Now, why is polling different?
Let’s apply some math. Suppose 60% of the M&Ms are red and 40% are blue, and the average guess over time hovers around those numbers. That works because the number of M&Ms is fixed. But in polling, Candidate A might be polling at 47%, and Candidate B at 45%, with a margin of error of ±3%. This means the real support for Candidate A could range from 44% to 50%, and for Candidate B, it could range from 42% to 48%.
Conclusion? The two candidates are statistically tied, even though the poll shows a slight lead for Candidate A. This nuance is often lost in media reports, which focus on who’s “winning” without acknowledging the margin of error.
Polling errors can sometimes reach as high as 10%, and this isn’t just an exaggeration. In recent elections, polling errors have been substantial. For example, in the 2020 U.S. presidential election, national polls overstated Joe Biden’s lead by an average of 3.9%, and in some swing states, like Wisconsin and Pennsylvania, the error margin was as high as 7%. Similarly, in the 2016 election, polls significantly underestimated Donald Trump’s support.
these large discrepancies arise from the cumulative effect of the assumptions we’ve discussed. Each small error—whether it’s an inaccurate sample, a misunderstood question, or a respondent who later changes their mind—adds up, leading to polling errors of 5%, 7%, or even 10%.
In addition to the common assumptions already mentioned, there are several other crucial assumptions that contribute to polling errors:
Pollsters often assume that voter turnout in the current election will mirror past elections. But turnout can vary significantly based on enthusiasm, voter suppression efforts, or changes in voting laws. If turnout is higher or lower than expected, the poll results will be off.
Pollsters tend to categorize respondents based on their stated party affiliation, assuming that most Democrats will vote for the Democratic candidate and most Republicans for the Republican candidate. However, party loyalty isn’t always guaranteed, and swing voters or independents can shift the outcome in ways the poll didn’t account for.
When pollsters encounter undecided voters, they often predict how they’ll vote based on past elections or demographics. But undecided voters are unpredictable. They may not decide until the very last minute, and when they do, their choice can be influenced by factors that are impossible to predict, such as a news event or debate.
Finally, pollsters assume that their methods—phone calls, online surveys, etc.—reach a representative sample of the population. But certain groups, like young people or minorities, may be less likely to participate in polls, leading to sampling biases.
Given all these assumptions, it’s no surprise that polls can be wildly off. But polls don’t just mislead analysts—they can affect voter behavior, too.
When people see that their candidate is ahead in the polls, they may feel less motivated to vote, thinking their candidate will win without their vote. On the flip side, if their candidate is behind, they may feel discouraged and decide not to vote at all. This is why relying on polls can be dangerous—it creates false expectations and can suppress voter turnout.
Polls are a useful tool for gauging public opinion at a given moment, but they are not crystal balls. The number of assumptions, potential errors, and the unpredictable nature of human behavior make polling an unreliable predictor of election outcomes. Instead of getting swept up in the latest polling numbers, it’s better to focus on the issues that matter, analyze broader voting trends, and listen to objective analysis.
When it comes to elections, your vote counts more than any poll ever will.
If you want to see an objective prediction of the election, click here.
