Inference and extrapolation

Last registered on January 17, 2022

Pre-Trial

Trial Information

General Information

Title
Inference and extrapolation
RCT ID
AEARCTR-0007006
Initial registration date
January 11, 2021

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
January 11, 2021, 6:55 AM EST

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

Last updated
January 17, 2022, 8:32 PM EST

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

Locations

Region

Primary Investigator

Affiliation
Stanford University

Other Primary Investigator(s)

PI Affiliation
Stanford University
PI Affiliation
London School of Economics

Additional Trial Information

Status
In development
Start date
2021-01-11
End date
2022-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We run an experiment that studies belief updating biases in inference and extrapolation problems.
External Link(s)

Registration Citation

Citation
Fan, Tony, Yucheng Liang and Cameron Peng. 2022. "Inference and extrapolation." AEA RCT Registry. January 17. https://doi.org/10.1257/rct.7006
Experimental Details

Interventions

Intervention(s)
We study biases in two kinds of belief updating problems: inference and extrapolation.
Intervention Start Date
2021-01-11
Intervention End Date
2022-01-31

Primary Outcomes

Primary Outcomes (end points)
Subjects' prior and posterior beliefs
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
None
Experimental Design Details
See intervention
Randomization Method
Randomization done by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No cluster
Sample size: planned number of observations
660 subjects
Sample size (or number of clusters) by treatment arms
200 subjects in the baseline treatment, 140 subjects in a treatment with binary signals, 100 subjects in a treatment where subjects answer the inference question and extrapolation question sequentially, 100 subjects in treatments with two performance indices
To explore the role of the conceptual distance between the updating question and the information provided, we will run two more treatments. One treatment (60 subjects) make explicit the equivalence between high quality and good performance. The other treatment (60 subjects) uses similar words to frame the firm quality and the firm performance.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Stanford IRB
IRB Approval Date
2020-04-13
IRB Approval Number
44866

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