Understanding Uncertainty

Last registered on June 23, 2025
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Pre-Trial

Trial Information

General Information

Title
Understanding Uncertainty
RCT ID
AEARCTR-0016245
Initial registration date
June 18, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 23, 2025, 11:50 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
June 23, 2025, 4:38 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
United Kingdom of Great Britain and Northern Ireland
Region

Primary Investigator

Name
Michael Mcmahon
Affiliation
University of Oxford
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Other Primary Investigator(s)

PI Name
Peter Rickards
PI Affiliation
University of Oxford
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PI Name
Michael McMahon
PI Affiliation
University of Oxford
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PI Name
Ryan Rholes
PI Affiliation
University of Mississippi
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PI Name
Matthew Naylor
PI Affiliation
University of Oxford
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Additional Trial Information

Status
On going
Start date
2025-06-16
End date
2025-12-31
Keywords
Behavior
Additional Keywords
uncertainty, education, expectations
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We are interested in the effect that communicating uncertainty has on the public's understanding of key messages, as well as how they use those messages going forward. Do they believe these messages more or less when uncertainty is communicated, and does communicating that uncertainty affect credibility in the future in the face of data outturns that do not align with forecasts.
External Link(s)

Registration Citation

Citation
Mcmahon, Michael et al. 2025. "Understanding Uncertainty." AEA RCT Registry. June 23. https://doi.org/10.1257/rct.16245-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We first provide to participants varying types of uncertainty communication mediums in the form of a survey. We will identify if there are considerable differences in how the public understands the uncertainty being communicated across these mediums. We run a survey of the public and then one of experts all anonymously recruited.

Next, we will run an experiment where we ask people to engage in a forecasting experiment where they are provided different types of communication about the future, some involving uncertainty and some not. We will compare how people use the information in the different treatments, as well as how credibility is influenced.
Intervention (Hidden)
Intervention Start Date
2025-06-17
Intervention End Date
2025-08-02

Primary Outcomes

Primary Outcomes (end points)
In the survey, self-reported understanding, uncertainty and objective measures of median values and uncertainty ranges to be taken from data in the chart.

In the experiment, we will use the variable that they are forecasting to assess (relative to a Bayesian benchmark and the control group) how they use information to update their incentivised point estimate beliefs and their own incentivised uncertainty beliefs.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In the survey, we provide different charts and images to participants and ask a slew of questions about understanding. This gives us some summary statistics and baseline data around which types of medium convey uncertainty.

In the experiment, we randomly allocate participants to one of many treatments first. They will continue through this treatment and then will be allocated to one other treatment afterwards. We are therefore using a between-groups identification method although some participants will see two types of treatment to increase the effective sample size.
Experimental Design Details
Randomization Method
Randomisation done by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
1800 individuals
Sample size (or number of clusters) by treatment arms
400-control, 400-treatment1, 600-treatment2 400-treatment3
Within treatments there are 4 variations so 100 per variation within treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Oxford Economics Department Research Ethics Committee
IRB Approval Date
2025-06-07
IRB Approval Number
2029961
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials