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Scientific Self-Correction: The Bayesian Way

Romero, Felipe and Sprenger, Jan (2020) Scientific Self-Correction: The Bayesian Way. Synthese. ISSN 1573-0964

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Abstract

The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to self-correct and to produce reliable knowledge. There are different approaches for addressing this challenge. Social reformists hypothesize that the social structure of science such as the credit reward scheme must be changed. Methodological reformists suggest changes to the way data are gathered, analyzed and shared, such as compulsory pre-registration or multi-site experiments with different research teams. Statistical reformists argue more specifically that science would be more reliable and self-corrective if null hypothesis significance tests (NHST) were replaced by a different inference framework, such as Bayesian statistics. On the basis of a simulation study for meta-analytic aggregation of effect sizes, we articulate a middle ground between the different reform proposals: statistical reform alone won't suffice, but moving to Bayesian statistics eliminates important sources of overestimating effect sizes.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Romero, Felipef.romero@uvt.nl
Sprenger, Janjan.sprenger@unito.it0000-0003-0083-9685
Keywords: statistical inference, Bayesian statistics, frequentist statistics, significance testing, replication crisis, reliability of science, statistical reform, scientific method
Subjects: General Issues > Experimentation
Specific Sciences > Probability/Statistics
Specific Sciences > Psychology
General Issues > Science and Policy
General Issues > Values In Science
Depositing User: Jan Sprenger
Date Deposited: 13 May 2020 03:43
Last Modified: 13 May 2020 03:43
Item ID: 17175
Journal or Publication Title: Synthese
Publisher: Springer (Springer Science+Business Media B.V.)
Subjects: General Issues > Experimentation
Specific Sciences > Probability/Statistics
Specific Sciences > Psychology
General Issues > Science and Policy
General Issues > Values In Science
Date: 2020
ISSN: 1573-0964
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/17175

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