Abstract
In recent US presidential elections, there has been considerable focus on how well public opinion can forecast the outcome, and 2016 proved no exception. Pollsters and poll aggregators regularly offered numbers on the horse-race, usually pointing to a Clinton victory, which failed to occur. We argue that these polling assessments of support were misleading for at least two reasons. First, Trump voters were sorely underestimated, especially at the state level of polling. Second, and more broadly, we suggest that excessive reliance on non-probability sampling was at work. Here we present evidence to support our contention, ending with a plea for consideration of other methods of election forecasting that are not based on vote intention polls.
Highlights
To understand voter choice in American presidential elections, we have come to rely heavily on public opinion surveys, whose questions help explain the electoral outcome
We offer a theoretical explanation for this error rather than the commonly-cited sources of polling error, which focus on poll mode or bias
The British Election Study (BES) and British Social Attitudes Survey (BSA) employed classic multi-stage stratified probability sampling in their investigations of the 2015 general election, achieving response rates of 56% and 51% (AAPOR Response Rate 1), respectively; the actual Conservative vote lead over Labour, was estimated by these surveys almost exactly, with BES at seven points and BSA at six points, so offering a telling contrast to the gross errors made in the commercial polling exercises (Sturgis, et al, 2016)
Summary
To understand voter choice in American presidential elections, we have come to rely heavily on public opinion surveys, whose questions help explain the electoral outcome. Since the media forecasts rely mostly on polls, any widespread polling error should generate considerable concern. We offer theoretical and practical support for this hypothesis and argue that because of the inability to sample from the population of actual voters, and the inability to quantify the error that stems from that problem, polls should not be relied upon as prediction tools. Let us work through an illustration where “civic-minded Jill” follows the news – the lead stories and the polls – to arrive at her own judgment about who is ahead, who is likely to win She checks RealClearPolitics aggregates daily, since the average percentages from available recent polls are readily understood.
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