That mysterious number on the bottom of the political polls, what is that?
That number is the margin of error, a term you’ll often see at the bottom of election polls as the presidential race heats up. It’s typically shown as +/- X% or points, but what does that actually mean? Simply put, the margin of error gives us a range of potential outcomes within which the true result likely falls. It accounts for the small discrepancies that come from surveying a portion of the population rather than everyone. In other words, it’s a measure of how much the results might differ if we polled the entire group rather than just a sample. With a race as tight as Harris v. Trump, the margin of error matters a great deal. You need to see who is ahead and is that number outside the margin of error.
In the New York Times/Siena College Poll conducted from September 29 to October 6, Kamala Harris leads Donald Trump 49% to 46%, with a margin of error of +/- 2.4 percentage points. This means the race could easily tilt in either direction since, when accounting for the margin of error, both candidates' true support levels may overlap. For data scientists that help conduct these polls, this tight poll is exactly why it is so difficult to say whether someone is “ahead” in the race. It’s a tight contest, and this margin of error helps to show just how competitive the race is across the nation.
Generally, a smaller margin of error suggests a higher degree of accuracy. This is achieved through:
- Larger Sample Size: More respondents mean a better representation of the population, reducing uncertainty in the poll’s results.
- Proper Sampling Techniques: Random sampling and other methods help ensure that the sample mirrors the population, reducing bias that could distort the findings.
A margin of error doesn’t just reflect precision—it also highlights the unpredictability of polling. It’s impossible to poll everyone, especially in a short period of time. Even in well-conducted polls, small shifts can still change the final outcome.
A smaller margin of error might sound great, but it doesn't mean a poll is bulletproof. Things like response bias, the way questions are worded, and even the timing of the poll can all throw things off. Take this off-hand example: if I asked you “is your boss supportive of your career goals” at the day after a yearly review that disappointed you, your response might be different than if I asked you the same question after the company’s holiday party. Or if I asked “is your boss a hinderance to your career’s future because of their terrible attitude” after that bad review, your response might change even more! The example is trying to get at the same question (i.e. what do you think of your boss and your career’s future), but the way it is asked and when—some of which might be outside of the pollster’s control—can change the results.
Polling is tricky business—just look at 2016! Almost no one, except Nate Silver at FiveThirtyEight, saw Donald Trump's win coming. That’s the funny (and frustrating) part about statistics and the margin of error—there’s always a small chance the actual outcome won’t align with the “statistically probable” one, no matter how confident we are. Polling is like a weather forecast: mostly right, but surprises can still happen!
