Related Experiment Video
Updated: Sep 9, 2025

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
Published on: July 29, 2020
Prebunking and credible source corrections increase election credibility: Evidence from the US and Brazil
John M Carey1, Brian Fogarty2, Marília Gehrke3
1Department of Government, Dartmouth College, 6108 Hinman, Hanover, NH, USA.
Abstract:
We investigate how to counter misinformation about voter and election fraud using data from the US and Brazil. Our study first compares two types of messages countering claims of widespread fraud: (i) retrospective corrections from credible sources speaking against interest and (ii) prebunking messages that prospectively warn of false claims about future elections and provide information about election security practices. In the US, each approach immediately increased election confidence and reduced fraud beliefs, with prebunking showing somewhat more durable effects. In Brazil, prebunking had positive immediate effects across measured outcomes, whereas those of the credible source corrections were less consistent. We then conducted an experiment in the US randomizing exposure to a persuasion forewarning before election security information is provided. Prebunking again increased confidence and decreased fraud beliefs but only when the forewarning was omitted, suggesting that novel factual information is responsible for the observed effects of the prebunking treatment.
More Related Videos
Related Concept Videos
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Confirmation Biases
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Margin of Error
Errors In Hypothesis Tests
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...

