Numeracy and Statistical Biases Among Journalists and their Public

• In Event: Challenges Associated with Media Coverage of Polls and Election Forecasts

Abstract

While the horse race has long been a feature of election coverage, unique in 2016 was an increased reliance on forecasting models in horse race coverage of the presidential election. Focusing on this type of quantitative information, however, requires both reporters and the public to engage in some statistical interpretation. However, research has shown that interpretation of statistics is often subject to various biases including denominator neglect, framing, and results salience. Are journalists more immune to such biases than the public? Drawing on work in decision-making, we use a raft of numeracy experiments to investigate the relationship between numeracy, statistical interpretation, and newsworthiness judgements across two samples: (1) professional journalists and (2) the public. In efforts to discern whether individual biases may intersect with statistical biases, we manipulate the political context of each treatment. Our results speak to the need for journalism schools to incorporate mathematical literacy in their training. Moreover, in addition to the call for media literacy, the public may benefit from more clarity in statistical coverage.

Authors

• Yanna Krupnikov, Stony Brook University

• John Barry Ryan, Stony Brook University

• Kathleen Searles, Louisiana State University