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A Bayesian decision-making framework for replication

Published online by Cambridge University Press:  27 July 2018

Tom E. Hardwicke ,
Michael Henry Tessler ,
Benjamin N. Peloquin  and
Michael C. Frank
Tom E. Hardwicke
Affiliation:
Meta-Research Innovation Center at Stanford (METRICS), Stanford School of Medicine, Stanford University, Stanford, CA 94305. tom.hardwicke@stanford.eduhttps://tomhardwicke.netlify.com/
Michael Henry Tessler
Affiliation:
Department of Psychology, Stanford University, Stanford, CA 94305. mhtessler@stanford.edubpeloqui@stanford.edumcfrank@stanford.eduhttp://stanford.edu/~mtessler/https://benpeloquin7.github.io/https://web.stanford.edu/~mcfrank/
Benjamin N. Peloquin
Affiliation:
Department of Psychology, Stanford University, Stanford, CA 94305. mhtessler@stanford.edubpeloqui@stanford.edumcfrank@stanford.eduhttp://stanford.edu/~mtessler/https://benpeloquin7.github.io/https://web.stanford.edu/~mcfrank/
Michael C. Frank
Affiliation:
Department of Psychology, Stanford University, Stanford, CA 94305. mhtessler@stanford.edubpeloqui@stanford.edumcfrank@stanford.eduhttp://stanford.edu/~mtessler/https://benpeloquin7.github.io/https://web.stanford.edu/~mcfrank/
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Abstract

Replication is the cornerstone of science – but when and why? Not all studies need replication, especially when resources are limited. We propose that a decision-making framework based on Bayesian philosophy of science provides a basis for choosing which studies to replicate.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e132
Copyright
Copyright © Cambridge University Press 2018 

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