How well do ordinary Americans forecast the growth of COVID-19?
COVID-19
Data visualization
Forecasting
Journal
Memory & cognition
ISSN: 1532-5946
Titre abrégé: Mem Cognit
Pays: United States
ID NLM: 0357443
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
accepted:
05
02
2022
pubmed:
30
3
2022
medline:
28
9
2022
entrez:
29
3
2022
Statut:
ppublish
Résumé
Across three experiments (N = 1565), we investigated how forecasts about the spread of COVID 19 are impacted by data trends, and whether patterns of misestimation predict adherence to social-distancing guidelines. We also investigated how mode of data presentation influences forecasting of future cases by showing participants data on the number of COVID-19 cases from a 5-week period in either graphical, tabular, or text-only form. We consistently found that people shown tables produced more accurate forecasts compared to people shown line-graphs of the same data; yet people shown line-graphs were more confident in their estimates. These findings suggest that graphs engender false-confidence in the accuracy of forecasts, that people's forecasts of future cases have important implications for their attitudes concerning social distancing, and that tables may be better than graphs for informing the public about the trajectory of COVID-19.
Identifiants
pubmed: 35349111
doi: 10.3758/s13421-022-01288-0
pii: 10.3758/s13421-022-01288-0
pmc: PMC8960688
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1363-1380Informations de copyright
© 2022. The Psychonomic Society, Inc.
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