As the 2026 midterm elections approach, skepticism about the accuracy of polling remains widespread among both Democrats and Republicans. Democratic voters remain wary after polling errors in 2016 and 2024 that underestimated electoral shifts, while former President Donald Trump and his Federal Communications Commission chairman have labelled pre-election polls as “fake” and “suppressive,” pointing to recent data fabrication scandals involving firms like Wedgewood Polls. Despite doubts, polls continue to play a crucial role in shaping expectations about election outcomes.

The polling profession experienced a major turning point after the 2016 election when many surveys significantly misjudged voter preferences. A notable example came from a New England pollster whose final pre-election survey in New Hampshire projected an 11-point lead for Hillary Clinton; the actual margin was less than one percentage point. The key issue was a failure to adjust polling samples for voters’ education levels—a demographic factor that proved critical yet was largely overlooked at the time.

Since then, pollsters have confronted numerous challenges stemming from changing communication habits and political attitudes. The widespread use of caller ID, abandonment of landlines, and general distrust in pollsters and media have resulted in sharply declining response rates, often below 5 percent and sometimes under 2 percent, forcing the industry to reconsider traditional sampling methods. Most modern polls rely on online panels of volunteers incentivized to participate, a shift that introduces further challenges in achieving representative samples.

This evolving landscape has emphasized the importance of statistical weighting—adjusting samples to better reflect the demographic composition of the electorate. For instance, weighting can correct the overrepresentation of college-educated respondents, as seen in the 2016 New Hampshire case, by balancing education levels to match the population. While many polls historically neglected such adjustments, recent data shows a significant increase in weighting for education. A review of polls conducted during the last weeks before the 2024 election revealed that three out of every four state polls incorporated education weights, compared to just one in four in 2016.

Pollsters have also increased the use of weighting based on party affiliation or past vote choice. While some analysts, including statistician Nate Silver, argue that this practice risks bias and effectively “cheating,” others contend it helps correct for underrepresentation of specific voter groups and improves accuracy. The absence of a standardized approach means pollsters apply different weighting schemes, contributing to variability in poll results.

An example of this effect appeared in early October 2024 in Iowa’s Senate race, where two reputable polls diverged: Marist University’s poll, adjusted for demographics including income, showed Democrat Josh Turek leading by three points, while CNN/SSRS’s poll, which weighted for past voting but not income, placed Republican Ashley Hinson ahead by two points. Additional complexity arises from “likely voter” models, which attempt to predict who will actually cast ballots; these are often less transparent than demographic weighting.

Transparency remains a concern, as many pollsters provide limited information about their weighting methods, making it difficult for experts and the public to fully understand the basis for a given poll’s results. Although leading polls may offer detailed methodological explanations, these can be lengthy and technical.

For consumers of polling data, it is important to recognize that each poll reflects a set of decisions by its creators about how to model the electorate. Differences among polls are not simply errors but represent varying approaches to adjustment and estimation. Aggregating polls can provide a broader picture of electoral trends, but divergences among individual surveys also convey meaningful information about the uncertainty in races.

Looking ahead to 2026, polling will remain a vital, though complicated, tool for gauging voter sentiment. Should polling outcomes surprise, it will likely reflect incorrect assumptions or modeling choices rather than a failure to incorporate appropriate adjustments. The industry has learned important lessons from recent elections and continues to refine its techniques in response to evolving political and technological realities.