Do Polls Succeed in Predicting the Winner of the 2024 U.S. Presidential Election?

As the 2024 U.S. presidential election approaches, questions are growing about the accuracy of polls, especially after repeated failures in polling predictions in recent years. Despite methodological challenges, polls remain an important tool for estimating how close the race is. But the question remains: will they be reliable this time, and can we depend on them to predict the winner?

A Historical Tool

Most people, from elites and intellectuals to media figures and ordinary citizens, often cite poll results when they align with their preferences, while strongly questioning them when they show opposition or depict them as a minority.

For decades, polls have generally helped politicians understand what the public wants and informed the public about the most popular candidate at the ballot box. As a result, interest in polls tends to intensify in the period leading up to elections, not only among politicians but also the general public.

On the other hand, polls are a key mechanism for gauging the impact of election campaigns and how the public responds to them. For this reason, politicians rely on them to shape their messages and direct their campaigns. Thus, polls are not just an objective tool for understanding voter trends; they also play a role, in one way or another, in influencing voter behavior and their expectations regarding election outcomes.

Polling, as we know it today, began in the 1930s in the United States, pioneered by George Gallup, Elmo Roper, and Archibald Crossley in collaboration with American media outlets. In these early stages, polls were conducted face-to-face in people’s homes by interviewers. With the spread of landline phones in American households, interviews shifted to telephone polling. Later, as mobile phones became widespread, they became the dominant method.

As financial pressures increased on polling companies and their media partners, a shift occurred toward online interviews using both websites and mobile applications. This raised important methodological questions about how representative the poll data is of society, as well as concerns over declining response rates. Today, most companies rely on a mix of several methods to collect polling data in an attempt to reach representative samples.

Declining Trust

In 2016, most polling organizations predicted a big win for Hillary Clinton, which did not happen. And while in 2020, most polls correctly predicted Joe Biden’s victory, they also overestimated the level of support for the Democratic candidate compared to then-President Donald Trump. These errors left many Americans disappointed with the polling industry as a whole. In recent years, public trust in polls has diminished.

These mistakes may be fresh in the minds of the American public, but the history of polling failures in U.S. elections dates back much further. One of the most famous examples is the 1948 election, where most polls predicted an easy victory for Republican candidate Thomas Dewey over Democrat Harry Truman. However, President Truman won the election. In the 1960 election, polls favored Richard Nixon, but John F. Kennedy emerged victorious. A similar scenario occurred in the 1980 election when polls suggested a close race between Jimmy Carter and Ronald Reagan, yet Reagan won by a large margin.

Types of Polls

Before delving into the reasons why polls sometimes fail to predict U.S. election results, it’s important to distinguish between three types of polls based on their credibility:

Bogus Polls: These polls do not represent a valid sample. They include any type of poll where respondents can participate multiple times, or where the sample does not accurately reflect the electorate as a whole. Examples include polls on social media platforms like Twitter/X, Facebook, and certain websites. Participants in these polls do not reflect the general population.

Problematic Polls: These are polls produced internally by candidates’ election campaigns. They are usually not released to the public unless they show favorable results for the sponsoring candidate. While these polls are not necessarily inaccurate or unscientific, the publication of internal campaign polls is selectively biased toward results that favor the candidate.

Scientific Polls: These polls are typically conducted by non-partisan institutions, universities, and polling organizations. They play an important role in providing objective measures of current events and the progress of candidates in elections. These polls are based on sound scientific methods and have successfully predicted many outcomes in the past. However, this does not mean they are immune to failure, as shown in several of the previously mentioned cases.

Methodological Issues

There are numerous challenges facing the process of public opinion polling, many of which are tied to complex factors that raise doubts or criticisms regarding the credibility of poll results. Some of the key questions include: Are the right questions being asked by pollsters? Are the questions being framed in a way that manipulates the responses to align with the desired outcome? Who are the people being interviewed? Who is funding these polls—political parties, media outlets, or lobbying groups?

The complexity increases when polls are tied to critical events such as the U.S. elections, where the goal is to predict the winner. This brings additional methodological challenges that affect the accuracy of such predictions. The most significant of these challenges include:

Unrepresentative Samples: Many analyses suggest that the main reason behind pre-election polling errors is related to the samples used. If the samples over-represent supporters of one party and under-represent those of the other, and the statistical weighting applied to the raw data is insufficient to correct this imbalance, the samples will not accurately reflect the electorate. As a result, the polls fail to make accurate predictions.

Margin of Error: All polling samples include a margin of error, even if the poll follows all scientific and methodological standards. This figure represents the “uncertainty” that arises from sampling a population instead of interviewing everyone. Random samples are likely to differ slightly from the overall population simply by chance. In the best-case scenario, a typical election poll sample of about 1,000 people will have a margin of error of plus or minus three percentage points. Additionally, there are three other equally important sources of error in polling:

First, coverage error, where not all members of the target population have an equal chance of being included in the survey.

Second, nonresponse error, where some groups are less likely to participate.

Third, measurement error, where respondents may not understand the questions correctly or may inaccurately report their opinions. The declared margin of error does not account for these other potential sources of error. As a result, the actual margin of error is typically twice the declared margin. Several studies show that the total error in poll estimates can be closer to twice the size of the commonly reported margin of error.

Weighting Voter Participation: Predicting who will actually vote is extremely challenging and is a fundamental issue that routine polling does not face. Every individual in the selected sample is asked which party they intend to vote for. However, not all respondents who are eligible to vote actually participate. Therefore, it is necessary to derive a subset of the sample that pollsters expect will vote. This is done by assigning an estimated probability of voting to each respondent, known as the “voter participation weight.” Polling organizations differ in how they derive voter participation weights, and there is no clear consensus on the accuracy of these weights.

The Swing Vote: Some individuals change their allegiance between parties and shift from non-committal responses like “I don’t know” or refusal to a particular party. Some voters agree to participate in polls but do not reveal which party they intend to vote for. Others may not yet know which party they will support or may change their minds late in the campaign. If enough of these voters—between the final polls and election day—shift disproportionately toward one party, the voting intention estimates in the polls will differ from the actual election results. Current methods for handling respondents who say they don’t know or refuse to disclose which party they plan to vote for remain improvised and lack a solid theoretical foundation.

The Spiral of Silence and the Bradley Effect: The “spiral of silence” refers to the phenomenon where respondents do not disclose the candidate they intend to vote for, especially if that candidate holds views that are racist, anti-religious, or could subject the voter to moral accusations. These respondents prefer to remain silent, fearing pressure or judgment, but reveal their true choice at the secret ballot box. In the current situation, with a female candidate, some people may idealistically respond that they will vote for Harris out of fear of being labeled sexist. However, it remains to be seen if American society is ready to be led by a woman. This scenario is known as the “Bradley Effect,” where voters lie to pollsters out of fear of being accused of racism or bias against a minority or social group. This was evident in the case of black American candidate Tom Bradley, who lost to his white Republican rival George Deukmejian, despite leading in the polls, in the 1982 California gubernatorial election.

Electoral Influences

With the repeated failures of opinion polls, many politicians have argued that inaccurate polls may have influenced election outcomes. Two potential effects of polls can be considered:

“Bandwagon” and “Underdog” Effects: Since the results of polls regarding voting intentions are often published, they can influence voters’ perceptions of a candidate’s chances of winning. This may, in turn, affect how people vote at the polls. When people vote for the candidate they believe will win, this is known as the “bandwagon effect.” Conversely, voters may evaluate candidates more negatively if their chances appear slim, known as the “underdog effect.”

Impact on Voter Turnout: There is evidence suggesting that when the public is told that a candidate is highly likely to win, some people may be less inclined to vote. After the 2016 election, many wondered whether the widespread predictions of Hillary Clinton’s guaranteed victory led some potential voters to conclude that the race was essentially over and that their votes wouldn’t make a difference.

This prompted some politicians to argue that polls undermine the electoral process. Overconfidence in Clinton’s success, as suggested by many polls, may have led her supporters to assume she would win and Trump would lose, causing voter complacency. In this way, the very polls predicting her victory may have been a factor in her defeat.

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

The simplest way to gather opinions, attitudes, or voting intentions from people is to ask them directly. Trying to decipher voting intentions from someone’s social media posts is less effective than just asking them. While election polls and surveys might seem old-fashioned, they are still the best way to gather public opinions. However, this doesn’t mean that they don’t require continuous methodological improvements to remain effective.

Predictive Capabilities

In conclusion, the key question remains: Can we rely on polls to predict the winner of the U.S. elections, despite these methodological issues?

Firstly, it’s important to note that the real value of polls is not in telling us who will win, but in estimating how close the race is. Nonetheless, it can be said that in races where the polling margin is less than three points, it is impossible to definitively predict the winner. Even if the election results align with the polls, it would be more a matter of coincidence than accuracy.

Even in races where the polling margin is between three and six points, the margin of error remains significant. Polls may be considered reasonably reliable if the margin between the candidates exceeds eight points in pre-election surveys, although this is not fully guaranteed due to the role played by the Electoral College in U.S. elections.

Polls give us a general sense of public opinion about presidential candidates, but the election outcome is determined by states through the Electoral College. The 2000 and 2016 elections revealed a difficult truth: a candidate who wins the largest share of the popular vote nationwide may still lose the election. In both of these elections, the national popular vote winners (Al Gore and Hillary Clinton) lost the Electoral College to George Bush and Donald Trump, respectively.

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SAKHRI Mohamed
SAKHRI Mohamed

I hold a Bachelor's degree in Political Science and International Relations in addition to a Master's degree in International Security Studies. Alongside this, I have a passion for web development. During my studies, I acquired a strong understanding of fundamental political concepts and theories in international relations, security studies, and strategic studies.

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