Media Stars: Statistical Significance and Research Impact

Abel Brodeur, Nikolai Cook, Anthony Heyes and Taylor Wright

No 254, I4R Discussion Paper Series from The Institute for Replication (I4R)

Abstract: How efficiently do scientific results make their way into the wider world? Applying multiple methods to the universe of hypothesis tests reported in three leading health journals between 2016 and 2022 we evidence the important role of statistical significance as a driver of popular attention to research results. For example, a research finding with significance that places it marginally inside the arbitrary 5% threshold attracts 60 to 110% more real world attention than one with significance marginally outside that threshold. We explore underlying mechanisms and argue that the results have important implications for the (in)efficiency of science translation.

Keywords: Hypothesis testing; Statistical significance; Knowledge mobilization; Popular science; News Media; Social Media; p-Hacking; Publication bias; Research credibility (search for similar items in EconPapers)
JEL-codes: B41 C12 I10 L82 (search for similar items in EconPapers)
Date: 2025
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Working Paper: Media Stars: Statistical Significance and Research Impact (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:i4rdps:254

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