As the 2024 U.S. presidential election looms on the horizon, skepticism surrounding the accuracy of opinion polls is mounting, fueled by their repeated failures in recent years. Despite grappling with methodological challenges, polls continue to serve as a crucial tool for gauging the closeness of the electoral race. However, a pressing question lingers: will they prove reliable this time? Can we confidently rely on these polls to predict the ultimate victor in the upcoming election?

A Historical Tool

People from all walks of life, including elites, intellectuals, journalists, and even ordinary citizens, cite poll results when they align with their views, while vehemently questioning them when they contradict their positions or depict them as a minority. For decades, polls have served as a crucial tool, helping politicians understand public sentiment and informing the electorate about the most popular candidates at the ballot box. Consequently, interest in polls typically surges in the lead-up to elections, captivating not only politicians but also the general public.

Moreover, polls function as a fundamental mechanism for gauging the impact of electoral campaigns and how the public interacts with them. As a result, politicians rely heavily on these surveys to craft their messages and steer their campaigns. Thus, polls are not merely a subjective tool for identifying voter trends; they also contribute, to some extent, to influencing voter behavior and shaping expectations regarding election outcomes.

The polls we recognize today originated in the 1930s in the United States, pioneered by George Gallup, Elmo Roper, and Archibald Crosley in collaboration with American media institutions. During these early stages, polls were conducted through face-to-face interviews at homes. As landline phones became prevalent in American households, the methodology shifted to this new mode of data collection. Subsequently, with the rise of mobile phones, this approach became the dominant method.

However, as financial pressures mounted on polling firms and their media partners, a transition occurred towards online interviews using both websites and mobile applications. This shift raised important methodological questions about the representativeness of polling data and the declining response rates. In response to these challenges, most firms currently employ a combination of methods to collect polling data, striving to reach representative samples and maintain the integrity of their surveys.

Declining Trust

In 2016, most polling institutions predicted a significant victory for Hillary Clinton, which did not materialize. Similarly, in 2020, while most polls correctly forecasted Joe Biden's victory, they also overstated support for the Democratic candidate compared to then-President Donald Trump. These consecutive errors left many Americans feeling disillusioned with the entire polling industry, contributing to a decline in the credibility of public opinion polls in recent years.

Although these mistakes may be fresh in the minds of the American public, the history of polling failures in American elections extends back much further. One of the most famous examples is the 1948 election, where most polls predicted a straightforward victory for Republican candidate Thomas Dewey over Democrat Harry Truman. Contrary to these predictions, President Truman emerged victorious against his rival.

This pattern of polling inaccuracies continued in subsequent elections. In 1960, polls had forecast a win for Richard Nixon, yet John F. Kennedy succeeded in clinching the presidency. Two decades later, during the 1980 election, a similar scenario unfolded. Polls indicated a close race between Jimmy Carter and Ronald Reagan, but the actual results showed Reagan winning by a substantial margin.

Polling Patterns

Before delving into the reasons for polling failures in predicting the outcomes of American elections, we can distinguish between three types of polls in terms of credibility:

1- Fake polls:

These do not represent a valid sample and include any type of polling where respondents can participate and vote multiple times or where the sample is entirely unrepresentative of the overall electorate. Examples include polls on social media platforms like Twitter/X and Facebook or other online polls, as the participants do not reflect the general population.

2- Questionable polls:

These are typically internal polls conducted by candidates' election campaigns. While not inherently wrong or unscientific, their credibility is compromised by selective release practices. Campaign teams usually withhold these polls from public view unless they showcase favorable results for their candidate. This selective disclosure creates a bias, as only polls that benefit the candidate tend to see the light of day, potentially skewing public perception.

3- Scientific polls:

 These are typically conducted by non-partisan institutions, universities, and polling organizations, and they play a significant role in providing objective metrics for current events and the progress of candidates in elections due to their solid scientific foundations. They have successfully predicted many outcomes in the past, although this does not negate the fact that they have also failed in numerous cases, as previously mentioned. 

Methodological Issues

Public opinion polling faces numerous dilemmas that affect its process, associated with complex dimensions raising criticism or questions about the credibility of polling results. Some of the most significant concerns include: Are pollsters asking the right questions? Are they adjusting question phrasing to obtain desired answers? Who are the interviewees Who funds the polls—political parties, media, or lobby and interest groups?

The situation becomes even more complicated when polls involve pivotal events like U.S. elections, where the task is to predict the winner. In such cases, another set of methodological issues emerges, affecting the possibility of achieving this task:

1- Unrepresentative samples:

Many analyses indicate that pre-election polling errors often stem from the samples relied upon. If these samples over-represent supporters of one party and under-represent another, and if the statistical weights applied to the raw data are insufficient to correct this imbalance, the samples would not accurately represent the voters, leading to failed predictions.

2- Margin of error:

All polling samples involve a margin of error, even when following all scientific and methodological standards. This deviation from the correct value expresses the "uncertainty" resulting from sampling rather than interviewing the entire population. Random samples might differ slightly from the population simply due to chance. In the best cases, a typical electoral survey sample of around 1,000 people has a sample error margin of plus or minus 3 percentage points. Moreover, three other equally important sources of error in polls exist which are: Coverage error (omissions or under coverage), where not all members of the targeted sample are included. Non-response error, where some demographics may be less likely to participate. Measurement error, where people may misunderstand questions or misreport their opinions.

Importantly, the declared margin of error does not account for these additional potential sources. Consequently, the actual margin of error is usually double the announced margin, with several studies showing that the total error in polling estimates can be closer to twice the size indicated by the typical sample error margin.

3- Voting participation weights:

Determining who will vote is extremely challenging—a fundamental issue not faced in routine polls. While all sample members are asked about their voting intentions, not all eligible respondents actually participate. Thus, it's crucial to derive a sub-sample of expected voters by assigning an estimated probability of voting to each respondent (known as voting participation weight). Polling institutions differ in their methods for deriving these weights, and there are no clear indications of their accuracy.

4- Swing voter block:

Some individuals may shift their positions between parties or move from non-objective responses (e.g., "I don't know" or refusal to answer) to supporting a specific party. Some voters agree to participate in polls but don't reveal their intended party, while others are undecided or change their minds late in the campaign. If a sufficient number of these voters shift disproportionately to one party between the final polls and election day, estimates of voting intentions will differ from the actual outcome. Current methods for addressing responses from undecided or non-disclosing participants lack a cohesive theoretical foundation.

Electoral Impacts

Repeated failures of polls have prompted many politicians to argue that inaccurate polls have affected election outcomes. Two potential impacts of polls can be outlined as follows:

1- "Bandwagon effect" and "backlash effects":

Since polling results about voting intentions are often published, they can influence voters' perceptions of a candidate's chances of winning, which in turn can affect how people vote at the ballot box. When people vote for the candidate they believe will win, this is referred to as the "bandwagon effect." Conversely, voters may view candidates more negatively if their chances seem weak, which is known as the "backlash effect."

2- Impact on turnout rates:

There is compelling evidence that when the public is informed that a candidate is likely to win, some people may be less inclined to vote. In the aftermath of the 2016 elections, many wondered whether numerous forecasts that seemed to guarantee Hillary Clinton's victory might have led some potential voters to conclude that the race was effectively over and that their votes would not make a difference. On the flip side, polls showing the race was close likely increased Trump's voter turnout.

This phenomenon has led some politicians to argue that opinion polls corrupt the electoral process. They suggest that the overconfidence in Hillary Clinton's success in the polls may have influenced her supporters' apathy, leading them to view her as the inevitable winner and Donald Trump as the certain loser. Consequently, these polls that predicted Clinton's victory became one of the factors that ironically contributed to her loss.

Despite some notable failures, polls will remain the most effective way to gauge voters' opinions, concerns on key issues, or voting intentions. While other data sources, such as social media, may provide some insight into voter behavior, they are not as reliable in predicting voting intentions or election outcomes, as they heavily rely on inference.

The most straightforward way to gather people's opinions, positions, or voting intentions is to ask them directly. Attempting to decipher voting intentions from a person's social media posts is inherently less effective than simply asking them outright. While election polls and polls in general may be considered old-fashioned by some, they continue to be the best method to obtain people's opinions. However, this does not negate the need for continuous methodological improvements to ensure they remain relevant and accurate in the ever-changing landscape of public opinion.

Predictive Capabilities

In conclusion, the most pressing question remains: can we rely on polls to predict the winner of the U.S. elections, despite all these methodological challenges?

Firstly, it is crucial to understand that the true value of polls lies not in definitively forecasting the winner, but rather in estimating the closeness of the race. With this in mind, it can be asserted that in election races where the polling difference is less than 3 points, it becomes virtually impossible to predict the winner with any certainty. Even if the election results align with the polls in such cases, it would be mere coincidence rather than a testament to polling accuracy.

Moreover, even in election races where the polling difference ranges between 3 and 6 points, the margin of error in the polls remains substantial. Only when the difference between candidates exceeds 8 points in pre-election polls can we begin to place reasonable confidence in their predictive power. However, it is important to note that even this level of certainty is not fully guaranteed, largely due to the unique role of the Electoral College in U.S. elections.

While polls provide us with valuable insights into the general public's opinion regarding presidential candidates, it is crucial to remember that the ultimate election outcome is determined by the states in the Electoral College. This distinction was starkly illustrated in the 2000 and 2016 elections, which revealed a hard truth: a candidate who secures the largest share of support among all voters in the U.S. may still lose the election. In both of these notable instances, the winners of the national popular vote, Al Gore and Hillary Clinton, ultimately lost in the Electoral College to George Bush and Donald Trump, respectively.