Emily Guskin, The Post’s deputy polling director, breaks down the world of political polling as Election Day nears. She explains how polls are conducted and emphasizes the significance of their methodology, including random sampling and question crafting. Emily tackles public skepticism about polling accuracy, especially post-2016, and discusses improvements made by pollsters to better capture voter demographics. With insightful analogies, she clarifies why polls reflect momentary public sentiment rather than future predictions.
Polling captures a snapshot of public opinion through randomized sampling, allowing understanding of current sentiments rather than future predictions.
The margin of error is essential for interpreting poll results, indicating the range within which true public attitudes may lie.
Deep dives
Understanding Polling Methodology
Polling provides insights into public opinion by systematically reaching a randomized sample of a target population. It allows pollsters to gather data on how groups, such as Nevada or North Carolina voters, feel about critical issues at a specific moment in time. The randomness of sampling means that even if an individual does not receive a call to participate, the collected data represents a broader sentiment. Polls pose questions that gauge immediate preferences, rather than predicting future outcomes, making it essential for consumers to interpret results with the understanding that public sentiment can shift quickly.
Interpreting Poll Results
Poll results should be understood as snapshots of current public attitudes rather than forecasts of future elections. The margin of error is a crucial aspect, indicating the potential range within which the true figures might lie; for example, an 18% approval rating with a 3% error margin means the actual approval could rest between 15% and 21%. Consumers should approach polls critically, noting discrepancies in results from various organizations and being wary of those with potential biases. Trustworthy polling sources release detailed methodologies, including sample sizes and question phrasing, enabling individuals to gauge the reliability of reported data.
Polling Challenges and Improvements
Polling accuracy has been under scrutiny since notable discrepancies in the 2016 election, resulting in a heightened focus on refining methods. Pollsters have adjusted their approaches, factoring in educational background and non-response bias to create representative samples. The challenges of predicting voter turnout from diverse demographics highlight the complexities of political polling, as the United States uses electoral rather than popular votes. Ongoing self-evaluations within polling organizations aim to enhance accuracy, as evidenced by improvements seen in the subsequent elections, demonstrating a commitment to restoring trust in polling data.
As Election Day looms, we’re seeing more and more headlines based on poll results. Some declare Trump and Harris neck and neck, while others state one candidate has a small advantage over the other. But how can we make sense of allthesepolls flooding the news cycle?
Today, Martine Powers speaks with The Post’s deputy polling director, Emily Guskin. Emily explains how a poll comes to be, what to look for when trying to understand whether a poll is trustworthy and breaks down once and for all what “margin of error” really means.
Today’s show was produced by Ariel Plotnick, with help from Bishop Sand. It was edited by Reena Flores and mixed by Sam Bair. Subscribe to The Washington Post here.
Correction: A previous version of this episode gave an example of a margin of error applying to a percentage of a sample that hated apples. In the example, the margin of error actually applied to the percentage of the population that hates apples. The audio has been corrected.
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