A controlled experiment with 1,012 people found that, when the same forecast is worded as "10% chance of losing" instead of "90% chance of winning," the voter whose candidate loses is far less likely to think the forecast was wrong, and a bit less likely to doubt the election's legitimacy.
When someone votes for the candidate who loses, they tend to distrust the process and the system more. This is called the winner-loser gap, and it's not a minor detail. It erodes perceived election legitimacy, and Toshkov and Mazepus (2022) found its consequences can even affect the health, happiness, and life satisfaction of those on the losing side.
Forecasts feed that tension. Communicating that a candidate has a "90% chance of winning" inflates the sense of certainty about the outcome. When the unlikely happens and the candidate loses, that surprise can turn into suspicion. As Mongrain (2023, as cited in the paper) notes, wrongly anticipating an outcome "tends to create a state of psychological discomfort, which might even breed denial."
Our question was direct. Can that distrust be reduced just by changing how the forecast is framed, without touching the number? That is, by communicating the same probability as "10% chance of losing" instead of "90% chance of winning."
We designed a controlled experiment, preregistered before collecting a single data point, and ran it with 1,012 people in the United States via Prolific.
It was a 2×3 design. The forecast was presented as probability of winning or its equivalent probability of losing, with three levels of favoritism: clear favorite (90/10), moderate favorite (75/25), or slight lead (60/40).
Each person read that the candidate they supported was the favorite according to the forecasts… and that they ultimately lost the election. Exactly the scenario that triggers distrust.
We measured two perceptions on a 1-to-7 agreement scale. Was the forecast wrong? Is the election's legitimacy doubtful? The higher the score, the more distrust.
The number didn't change. "90% winning" and "10% losing" are identical; the only thing that changed was which word carries the focus, and that alone was enough to substantially shift how much the losing voter trusts the forecast.
It wasn't a blind bet. The design builds on prior findings: how a probability is worded shapes what people expect, beyond the number itself (Teigen & Brun, 1999). And across different contexts, negatively worded information and messages tend to be perceived as more truthful and trustworthy (Hilbig, 2009, 2012; Koch & Seeger, 2017). The paper reviews several more mechanisms; these two were the starting point.
How lopsided the forecast was also matters. When there was a clear or moderate favorite, agreement that "the forecast was wrong" was higher than when the lead was slight; between the clear and moderate favorite conditions, the difference wasn't statistically significant.
It makes sense. The more a victory was anticipated, the more surprising the defeat, and the easier it is to think the forecast was wrong.
One of the study's attention checks asked people to recall the exact forecast number. Some people, instead of repeating the number they'd read, wrote its complement: if they'd been told "10% chance of losing," they answered "90% chance of winning."
The study was preregistered on OSF (hypotheses, design, and analysis plan fixed before collecting data, so results couldn't be cherry-picked after the fact). We calculated sample size with a power analysis and, out of 1,219 responses, kept 1,012 after strict attention-check filters. Of the two preregistered hypotheses, we confirmed that framing affects perception; we did not confirm that favoritism moderates that framing effect, since the interaction between both factors wasn't significant once covariates were included. The framing main effect holds even controlling for gender, age, education, and political affiliation, so it isn't explained by participants' profile.
In an exploratory, non-preregistered analysis, framing also reduced suspicion about the election's legitimacy, but only when there was a clear or moderate favorite, not when the lead was slight; since this came after the data, more evidence is needed to confirm it. We detail other limits of the study below.
It's a working paper and hasn't gone through peer review yet; that's why we're keeping the data and materials open.
Being honest about limitations is part of the method. These are the ones the authors themselves acknowledge in the paper.
Communicating a forecast as "probability of losing" costs nothing and misleads no one, because it's exactly the same probability. But it can measurably reduce the distrust of whoever loses after an unexpected result.
For pollsters, media outlets, or forecasters, it's a concrete lever, a wording decision backed by experimental evidence that can soften the blow to trust in the system, especially in the single-probability format we tested here. It doesn't solve polarization, but it moves part of the problem at the cost of changing a sentence.
This study also shows how the lab works. We start with a question, turn it into a preregistered hypothesis, test it with a controlled experiment, and measure the effect. It's the same method we apply with our clients, here applied to a question of public interest.