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609

Title

Representativeness heuristic in intuitive predictions: Replication Registered Report of problems reviewed in Kahneman and Tversky (1973)use asterix (*) to get italics

Authors

Hong Ching (Bruce) Chan, Gilad FeldmanPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year

2024

Picture

Abstract

[IMPORTANT: Abstract, method, and results were written using a randomized dataset produced by Qualtrics to simulate what these sections will look like after data collection. These will be updated following the data collection. For the purpose of the simulation, we wrote things in past tense, but no pre-registration or data collection took place yet.] The representativeness heuristic is the phenomenon that people make predictions not by statistically considering prior evidence, but by the representativeness of the evidence to the target of prediction . In a Registered Report experiment with a US American sample on Prolific (N = 1309), we conducted a conceptual replication of Studies 1 and 2 and a close replication of Studies 3 to 7 from Kahneman and Tversky (1973). [The following is a demo placeholder based on the random simulated and will be updated following data collection.] We found support for the effects of [...] (effects + 95% CI). We found mixed support for the effects of [...] (effects + 95% CI). However, we failed to find support for the effects of [...] (effects + 95% CI). Extending the replication, we [found/failed to find] support for [...]. Overall, we concluded that [...]. Materials, data, and code are available on: https://osf.io/8zhcj/

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Keywords

representativeness, heuristic, predictions, statistical regression

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields

Social sciences

Suggested reviewers - Suggest up to 10 reviewers (provide names and Email addresses). (Optional)

Omid Ghasemi [o.ghasemi@unsw.edu.au] suggested: Robert Ross, rob.ross@mq.edu.qu , Falk Lieder [falk.lieder@tuebingen.mpg.de] suggested: Joshua Lewis: joshua.lewis@nyu.edu , Falk Lieder [falk.lieder@tuebingen.mpg.de] suggested: Gilad Feldman: giladfel@gmail.com , Falk Lieder [falk.lieder@tuebingen.mpg.de] suggested: Craig Fox: craig.fox@anderson.ucla.edu , Falk Lieder [falk.lieder@tuebingen.mpg.de] suggested: Uri Simonsohn: urisohn@gmail.com , Falk Lieder [falk.lieder@tuebingen.mpg.de] suggested: Leif Nelson: leif_nelson@haas.berkeley.edu , Falk Lieder [falk.lieder@tuebingen.mpg.de] suggested: Joe Simmons: jsimmo@upenn.edu No need for them to be recommenders of PCI Registered Reports. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

Opposed reviewers - Suggest up to 5 people not to invite as reviewers. (Optional)

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Submission date

2023-11-29 15:19:07

Recommender

Rima-Maria Rahal

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