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NewsMaterial3 min readDisconfirmAI Editorial

Barry Ritholtz: Why Polls Keep Failing — The Three-Layer Gap Between Attitude, Intention, and Behavior

Barry Ritholtz attributes polling failures to a three-layer gap in respondent psychology: people do not truly know what they believe (attitude layer), the cost of stating intentions is zero and often carries a bandwagon bias (intention layer), and self-reported voting intention significantly overestimates actual turnout (behavior layer). He explicitly notes that this topic is closely linked to errors in market and economic forecasting but does not elaborate on specific investment applications in this article.

2026-11-03 20:30 GMT+8

In an article published on November 3, 2026, Barry Ritholtz presents a core judgment regarding prediction failure: pollsters mistakenly believe their core work is data analytics, but history suggests it is more akin to the work psychologists do when trying to predict future behavior. Failing to discern this distinction is why polling track records have fluctuated from decent (2024) to abysmal (2016, 2020). He states at the outset that his motivation for writing is that this topic fascinates him because it closely tracks prediction errors in markets and macroeconomic forecasts.

Ritholtz breaks down respondent error into three layers. The first is attitude: people do not truly know what they believe. Many individuals create unstable answers on the spot for policy issues they have never considered; these are known as "non-attitudes." Others use polls to send messages rather than report true preferences—what he calls expressive answering. Ambivalent voters who like one candidate’s personality but another’s policies give squishy answers that they themselves may later revise.

The second layer is intention: people declare with high confidence that they will do something, yet the cost of making that statement is zero. Ritholtz writes that we tend to say what we want to believe or what we think the listener wants to hear. Expressing support costs the respondent nothing; their commitments may be conditional or aspirational. Additionally, there is the bandwagon effect: some state their preference based on who they perceive will win, because people do not want to back a loser.

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The third layer is behavior, which is the largest gap: self-reported voting intention significantly overstates actual turnout. Ritholtz cites examples such as the 2022 U.S. midterm elections, where a "red wave" failed to materialize, while 2024 saw the reverse. Low-propensity voters who show up only for a specific candidate are precisely the people likely-voter models often exclude, and the results of model adjustments have been decidedly mixed. More fundamentally, between answering and voting, factors such as weather, breaking news, geopolitics, the economy, and inflation can intervene.

Ritholtz explicitly defines the boundaries of his discussion in the article: he does not address whether pollsters reached a representative sample (he believes they did not) nor whether the questions themselves were biased (he believes it varies by situation). His argument is that the better pollsters understand respondent psychology, the more accurate their numbers will be. At the end of the article, he quotes the classic saying—"all models are wrong, but some are useful"—and points out that models assume the future looks like the past, an assumption that is particularly problematic during periods of disruption.

For investors, the transfer value of this framework lies in the ability to determine which layer—attitude, intention, or behavior—a given piece of stated data belongs to when faced with any data based on statements. Ritholtz himself only briefly noted the parallel relationship between polling errors and those in market and macroeconomic forecasts with a single sentence, without elaborating on specific investment applications in this article. Voting and investment decisions face different incentive structures—voters express opinions at zero cost, while investors involve real financial risk—so this analogy is heuristic rather than causal.

Sources

  • ritholtz.comThe Big Picture↗
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