Should We or Should We Not Include Confidence Intervals in COVID-19 Death Forecasting? Evidence from a Survey Experiment

Autor: Jean-François Daoust, Frédérick Bastien
Rok vydání: 2020
Předmět:
2019-20 coronavirus outbreak
History
medicine.medical_specialty
Sociology and Political Science
Coronavirus disease 2019 (COVID-19)
Polymers and Plastics
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
media_common.quotation_subject
education
forecasting
050801 communication & media studies
Affect (psychology)
Industrial and Manufacturing Engineering
0508 media and communications
Order (exchange)
Perception
Statistics
Pandemic
050602 political science & public administration
medicine
survey experiment
Business and International Management
uncertainty
confidence intervals
health care economics and organizations
Reliability (statistics)
media_common
projections
Actuarial science
Public health
media
05 social sciences
COVID-19
Survey experiment
Confidence interval
0506 political science
Geography
Political Science and International Relations
Norm (social)
Psychology
Inclusion (education)
Zdroj: Daoust, J-F & Bastien, F 2021, ' Should we or should we not include confidence intervals in COVID-19 death forecasting? Evidence from a survey experiment ', Political Studies Review . https://doi.org/10.1177/1478929920985686
ISSN: 1556-5068
DOI: 10.2139/ssrn.3703966
Popis: Forecasting during the COVID-19 pandemic entails a great deal of uncertainty. The same way that we would like electoral forecasters to systematically include their confidence intervals to account for such uncertainty, we assume that COVID-19-related forecasts should follow that norm. Based on literature on negative bias, we may expect the presence of uncertainty to affect citizens’ attitudes and behaviours, which would in turn have major implications on how we should present these sensitive forecasts. In this research we present the main findings of a survey experiment where citizens were exposed to a projection of the total number of deaths. We manipulated the exclusion (and inclusion) of graphically depicted confidence intervals in order to isolate the average causal effect of uncertainty. Our results show that accounting for uncertainty does not change (1) citizens’ perceptions of projections’ reliability, nor does it affect (2) their support for preventive public health measures. We conclude by discussing the implications of our findings.
Databáze: OpenAIRE
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