Partisan mathematical processing of political polling statistics: It's the expectations that count.

In this research, we investigated voters' mathematical processing of election-related information before and after the 2012 and 2016 U.S. Presidential Elections. We presented voters with mental math problems based on fictional polling results, and asked participants who they intended to vote for and who they expected to win. We found that committed voters (in both 2012 and 2016) demonstrated wishful thinking, with inflated expectations that their preferred candidate would win. When performing mathematical operations on polling information, voters in 2012 and 2016 deflated support for the opponent. Underestimation of the opponent was found to be absent among the participants who did not expect their preferred candidate to win. Identical experiments conducted after the elections revealed that partisan mathematical biases largely disappeared in favor of estimates in alignment with reality. Results indicate that mathematical processing of political polling data is biased by people's voting intentions and wishful thinking, and, crucially, by their expectations about the likely or actual state of the world.