Whenever you hear discussions about polling in the highly competitive presidential race between Vice President Kamala Harris and former President Donald Trump, the phrase “within the poll’s margin of error” often comes up. This term indicates that the contest is extremely close with no definitive frontrunner, even if one candidate holds a slight edge in support, such as 48% versus 47%.
As the head of the Quinnipiac University Poll, which has assessed public sentiment on various policies and elections for over 30 years, I’ve noticed that since 2016, people have been particularly attuned to this technical term.
During that election year, some Florida polls suggested that Hillary Clinton had a slight lead over Trump by a couple of percentage points. However, both journalists and the public frequently – and mistakenly – interpreted this lead as an indicator that Clinton was likely to clinch victory.
Yet, that 1 or 2 percentage point advantage was still within the margin of error for those polls, and ultimately, Clinton lost Florida. This margin of error informs readers of the potential range for election outcomes based on the sample surveyed.
Understanding the Margin of Error
A poll consists of one or more questions posed to a small group, aiming to gauge the opinions of a larger population. The margin of error is a statistical estimate that reflects the accuracy of the poll results – specifically, how closely the surveyed group’s answers align with those of the broader population.
If you could survey the entire larger population, there would be no margin of error. However, reaching everyone is challenging, difficult, and costly. For instance, the U.S. Census Bureau invested $13.7 billion over several years in its latest effort to count every individual in the United States every ten years and still wasn’t able to include everyone.
Pollsters don’t possess that type of time or budget, so they utilize smaller population samples. They strive to gather representative samples where all members of the broader group have a chance of being included in the poll.
The Impact of Sample Size
The precision of a poll’s results largely hinges on the size of the sample surveyed. For example, a sample of 600 voters will yield a margin of error of about 4 percentage points, while a sample of 1,000 voters will have a margin of error just over 3 percentage points.
The method used for selecting the sample is also crucial. In 1936, the Literary Digest magazine conducted a presidential election poll by mailing surveys to affluent individuals, such as telephone owners, car owners, and country club members. Since this sample represented only a wealthy demographic, it was not at all reflective of the entire U.S. voting population, making calculating a margin of error irrelevant.
Zieben007 via Wikimedia Commons, CC BY-SA
Breaking Down the Margin of Error
Let’s clarify how to interpret the margin of error. If a poll indicates that 47% of respondents favor Candidate A, and the margin of error is plus or minus 3 percentage points, that implies support for Candidate A among the general population likely falls between 44% (47 minus 3) and 50% (47 plus 3).
It’s worth noting that most polls pair their margin of error with another technical term, “confidence interval.” In a rigorous poll report, you might find something like, “The margin of error is plus or minus 3 percentage points, with a 95% confidence interval.” This means that if you were to conduct 100 random samples of the same size and asked the same questions, 95% of those samples would likely yield results within 3 percentage points of the reported figures.
Assessing Candidate Support
The idea of margin of error becomes more intricate when comparing support levels between candidates. If the margin of error is plus or minus 3 percentage points, the margin on the difference between candidate support levels is roughly twice that – around 6 percentage points in this case.
That’s because this margin of error encompasses not just the percentage for Candidate A but also that for their opponent.
Reflecting back on the 2016 election, the final Quinnipiac University Poll in Florida right before Election Day showed Clinton with 46% support and Trump with 45%. With a margin of error of 3.9 percentage points, that meant Clinton could expect between 42.1% and 49.9%, while Trump was anticipated to secure between 41.1% and 48.9%.
Ultimately, Trump garnered 48.6% of the vote in Florida, against Clinton’s 47.4%. These results fell within our poll’s margin of error, justifying our characterization of the race as “too close to call” and highlighting that claiming Clinton was in the lead would have been incorrect.
The Outlook for 2024
As we move through the current election cycle, there’s a concerning trend wherein many media outlets report on polls without including the crucial margin of error information.
Omitting or downplaying this information simplifies the narrative, allowing media platforms to present a clearer image of the race’s status. In an age dominated by technology and artificial intelligence, data can appear incredibly precise.
However, polling isn’t as exact as it might seem. It constitutes an inexact science, relying on pollsters to gather snapshots of the complexities of human opinion at a specific moment. People’s views can change, and new insights can emerge as election campaigns progress.
With the presidential election drawing near, our polling indicates a steady and tightly contested race, with many voters expressing firm decisions. Given that the differences between the candidates are within the margin of error in swing states, fall 2024 polling suggests that Americans should be prepared for a nail-biter of an election.
