Over the weekend, two new polls for the 2024 general election were released, and they presented different sets of numbers. One poll, conducted jointly by Hart Research Associates (a Democratic pollster) and Public Opinion Strategies (a Republican firm) on behalf of NBC News, indicated that President Biden and former President Donald Trump were tied at 46 percent among registered voters. In contrast, another survey from ABC News and The Washington Post showed Trump with a 9-point lead (51 percent to 42 percent) among adults. (Full disclosure: 538 operates as a political data journalism vertical under ABC News and has collaborated with both ABC News and The Washington Post on previous national polls.)
The latter poll’s findings not only diverged from the NBC News survey but also from a simple average of all 2024 general election polls that I calculated. Prior to the release of these two polls, this average, which accounted for recency and sample size, indicated that Biden was leading Trump by approximately 2 points. In its analysis of the poll, The Washington Post acknowledged, “The difference between this poll and others, as well as the unusual makeup of Trump’s and Biden’s coalitions in this survey, suggest it is probably an outlier.
Indeed, even reputable pollsters can occasionally produce outlier results. But how does this occur? How can we discern when statistical error might be influencing a poll’s outcome? And how should we approach and utilize these outliers, if at all? Allow me to explain my approach to addressing these questions.
Understanding the Factors Behind Outlier Polls
To comprehend why one poll can produce significantly different results from others, it’s crucial to delve into the mechanics of how polls are conducted. For the sake of clarity, we’ll focus on telephone surveys, as both the ABC News/Washington Post and NBC News polls were conducted in this manner.
The polling process kicks off with pollsters acquiring a list of potential interviewees’ phone numbers. There are a couple of methods for this. Some pollsters employ a technique called random digit dialing (RDD), where a computer randomly generates a list of both landline and cell phone numbers to dial. Others adopt registration-based sampling (RBS), where they retrieve phone numbers from voter registration records published by each state.
Here lies the first potential source of error in a poll, referred to as “coverage error.” In both RDD and RBS methods, a portion of the population is excluded from potential polling, as they are not within the poll’s sampling frame. Individuals without phone numbers fall into this category and are excluded from both RDD and RBS polls. RBS further narrows the pool to individuals who have registered to vote. According to the Pew Research Center, this encompasses slightly less than half of U.S. adults, though typically this does not significantly impact poll results. Nonetheless, you wouldn’t employ an RBS poll to assess the views of unregistered American citizens. Additionally, voters may register with no phone numbers or non-functioning ones, rendering them unreachable by pollsters.
Subsequently, the pollster conducts interviews with a specific number of individuals from their list, generally around 1,000 participants for large national polls. This presents the second potential source of error known as “sampling error.” The United States is a vast country, and a pollster’s sample of 1,000 individuals may not fully represent a population of 258 million adults. Pollsters employ a statistical measure called the margin of error to gauge this potential error. In the most recent ABC News/Washington Post poll, the margin of sampling error hovers around 3.5 points. This means that if the poll were repeated numerous times with different samples, Trump’s 51 percent could range from as high as 54.5 percent to as low as 47.5 percent. Likewise, Biden’s support could range from as high as 45.5 percent to as low as 38.5 percent.
However, even when considering Biden’s best-case scenario from the ABC News/Washington Post poll’s margin of error — a 2-point Trump lead — it still doesn’t align with the current polling average of Biden+2. This suggests that some other form of error is likely influencing these numbers.
This leads us to the third type of error to consider: “nonresponse bias.” This occurs when certain groups of people are less inclined to respond to polls compared to others. Perhaps they decline to pick up the phone, or they answer the call, realize it’s a pollster, and promptly hang up. However they arrive at this point, these non-responders may differ from the individuals who do participate in polls, introducing potential complications. For instance, studies by Pew have revealed that older voters and politically engaged individuals are more likely to respond to polls. Similarly, the Census Bureau has determined that white individuals and educated, high-income individuals are more likely to respond. While pollsters have statistical tools to address these demographic biases, they have limited remedies for bias resulting from certain political groups being more or less inclined to participate in polls, known as “partisan nonresponse bias.”
It is plausible that this partisan nonresponse bias played a role in the ABC News/Washington Post poll published recently. Upon analyzing the poll’s “crosstabs” (a document breaking down poll results by different demographic categories), it becomes evident that Biden struggles notably among key Democratic demographic groups. For instance, among voters aged 18-29, 41 percent expressed a preference for Biden if the election were held that day, while 48 percent favored Trump — a -7 point margin for Biden. However, according to a 2020 ABC News exit poll conducted by Edison Research, Biden held a 24-point advantage in that group during the 2020 election. This represents a 31-point decline in the margin, which is more than double the shift observed in the overall population. Biden also experienced significant drops in support among Latinos, urban residents, and Black respondents.
The Impact of Outlier Polls on Public Perception
Outlier polls, those that deviate significantly from the polling average, can have a substantial impact on public perception, especially in the era of instant news and social media. Here, we’ll discuss how outlier polls affect public opinion, media coverage, and their implications for political campaigns.
1. Influence on Public Perception
Outlier polls often garner significant attention from the public. When a poll is released showing an unexpected shift in support for a candidate or a significant change in a particular issue’s popularity, it can create a sense of momentum or concern among the electorate. This can influence undecided voters, donors, and campaign strategies.
Consider the scenario of an outlier poll showing a significant lead for Candidate A, while most other polls indicate a tight race. This outlier can lead some voters to believe that Candidate A is the inevitable winner, potentially swaying their vote or discouraging them from participating in the election altogether. It can also motivate supporters of Candidate B to work harder to reverse the perceived momentum.
Furthermore, outlier polls can reinforce preexisting biases and partisan beliefs. Supporters of Candidate A may eagerly embrace the outlier poll as evidence of their candidate’s superior appeal, while dismissing the consensus among other polls as biased or inaccurate.
In contrast, those who support Candidate B may view the outlier poll with skepticism, accusing it of being an outlier due to methodological flaws or bias. This polarization of opinion can contribute to a more divided and contentious political landscape.
2. Impact on Media Coverage
Outlier polls tend to generate media attention. News outlets often prioritize sensational or unexpected findings to attract viewership and readership. As a result, outlier polls can become the centerpiece of news coverage, overshadowing more consistent polling data.
Media outlets may use outlier polls as a basis for their narratives and analysis. For instance, if an outlier poll shows a significant shift in support for a particular policy, it can lead to in-depth coverage and discussions about the potential implications of this outlier result.
However, the media’s focus on outlier polls can be a double-edged sword. While it may increase public awareness of a particular issue or candidate, it can also contribute to confusion and misinformation if the outlier poll’s findings are not adequately contextualized. Media outlets have a responsibility to provide a balanced and informed perspective on outlier polls, acknowledging their divergence from the polling average and their potential sources of error.
3. Implications for Political Campaigns
Political campaigns pay close attention to polling data, including outlier polls. When an outlier poll suggests a significant advantage or disadvantage for a candidate, campaigns may adjust their strategies accordingly.
For a candidate leading in an outlier poll, the campaign may choose to emphasize the positive findings in their messaging and fundraising efforts. They might also target specific demographics or regions highlighted by the outlier poll as sources of strong support.
Conversely, a candidate trailing in an outlier poll may face increased pressure to reassess their campaign strategy. They may allocate additional resources to critical battleground states or demographics where the outlier poll suggests they are underperforming.
However, campaigns must exercise caution when reacting to outlier polls. Making hasty decisions based solely on the results of a single poll, especially an outlier, can be risky. Polls are snapshots in time and subject to various forms of error. Overreacting to an outlier poll can lead to misallocation of resources and strategic missteps.
4. The Role of Poll Aggregators
To mitigate the impact of outlier polls, many political analysts and media outlets rely on poll aggregators. Poll aggregators compile data from multiple polls, applying statistical methods to provide a more accurate and comprehensive view of public opinion.
Aggregators help smooth out the noise created by outlier polls by giving more weight to polls with established credibility and a track record of accuracy. They provide a more stable and balanced representation of the political landscape.
Which uses a weighted average approach to calculate polling averages and incorporate uncertainty measures. By doing so, they offer a more nuanced perspective on the state of the race, considering both the polling average and the potential variability introduced by individual polls.
Aggregators serve as a valuable resource for both the public and campaigns, offering a more reliable basis for understanding public sentiment and making informed decisions.
5. The Responsibility of Pollsters
Pollsters themselves bear a significant responsibility in preventing outlier polls from distorting public perception. Maintaining rigorous methodological standards, transparency, and ethical practices is essential for preserving the integrity of polling.
Pollsters should be cautious about releasing polls with results that deviate significantly from their own past findings or the consensus of other reputable polls. If they do publish outlier results, they should provide detailed explanations of their methodology and any unique circumstances that may have influenced the findings.
Additionally, pollsters must resist the temptation to engage in “herding,” a phenomenon where pollsters adjust their results to match the consensus of other polls, even if their data does not support such adjustments. While herding may reduce the prevalence of outliers, it risks creating a false consensus and undermining the diversity of voices in polling.
6. The Inherent Uncertainty of Polling
It’s important for the public to recognize that polling inherently involves a degree of uncertainty. No poll, even when conducted meticulously, can perfectly predict future election outcomes or the opinions of the entire population.
Outlier polls are a reminder of this inherent uncertainty. They highlight the variability that can arise from different sampling methods, response rates, and other factors influencing polling outcomes. Rather than viewing outlier polls as definitive, the public should approach them with a degree of skepticism and seek context from poll aggregators and experts.
7. Conclusion
In the era of data-driven politics, polls play a vital role in shaping public perception, influencing campaign strategies, and informing media coverage. However, outlier polls can create confusion, polarization, and misinterpretation of public sentiment.
To mitigate the impact of outlier polls, the public should rely on poll aggregators that provide a more stable and balanced view of the political landscape. Campaigns must exercise caution when reacting to outlier polls, recognizing their inherent variability.
Pollsters should maintain rigorous standards and transparency to uphold the integrity of polling, while the media must responsibly contextualize and report on outlier polls.
Ultimately, understanding the factors behind outlier polls is essential for a more informed and
resilient democratic process. It reminds us that polling, while valuable, is an imperfect tool for gauging public sentiment, and a nuanced and critical approach is necessary for interpreting its results.
Navigating Outlier Polls: What to Do with Unusual Data
In the realm of political polling, outliers occasionally emerge, offering unique insights or confounding results. When confronted with such outliers, it’s crucial not to overly rely on a single survey for a comprehensive understanding of political dynamics. At the same time, discarding data outright is not a wise practice, as it can lead to problematic trends like “herding,” where pollsters adjust their results to match others, ultimately reducing the accuracy of polling averages.
Here at 538, we advocate for a balanced approach: we incorporate outlier polls into our averages, acknowledging their presence while accounting for potential statistical adjustments. In high-profile elections like the 2024 presidential race, where an abundance of data is available, random outliers exert minimal influence on the overall average. Moreover, we employ statistical Bayesian principles, emphasizing the importance of adjusting our prior beliefs based on the plausibility of a poll’s results. In essence, polls within the standard range around our existing average are granted full weight, while those significantly deviating from this range are assigned less weight.
However, when a pollster consistently releases outlier results, it suggests a potential issue with their methodology rather than random variation. Although three such polls aren’t sufficient for definitive conclusions, they provide an indication that something about the methodology may skew the results. It’s important to note that deviations from the average don’t necessarily imply methodological flaws; instead, they underscore the need to consider methodological variations in our analysis.
To address this, we introduce a concept known as “house effects” into our modeling. House effects involve adjusting a pollster’s data based on our perception of their methodology’s leanings. This approach allows us to make informed adjustments when a pollster consistently demonstrates a proclivity for one side or the other. In the case of ABC News/Washington Post polls, their historical track record of accuracy merits attention. While future outliers will be approached with a degree of skepticism, their past reliability ensures their data remains a valuable component of our models.
In conclusion, outlier polls should be viewed with discernment, neither dismissed nor overemphasized. By striking a balance between considering these outliers and applying statistical adjustments, we aim to provide a more accurate representation of public opinion and political trends.
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