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Assessing evidence for replication: A likelihood-based approach.

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  • 1Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada. peter.dixon@ualberta.ca.

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Summary
This summary is machine-generated.

A new likelihood-based method evaluates experimental replications by comparing a theoretically interesting effect size to a null model. This approach reveals that many replications fail to provide evidence for the original findings.

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Area of Science:

  • Experimental Methodology
  • Psychological Science
  • Reproducibility in Science

Background:

  • Evaluating the success of scientific replications is crucial for methodological rigor.
  • Existing methods for assessing replication may not fully capture the evidence for theoretical effects.
  • The Replication Project highlighted challenges in reproducing scientific findings.

Purpose of the Study:

  • To develop a novel likelihood-based approach for assessing evidence of replication.
  • To provide a quantitative method for contrasting a theoretically derived effect size against a null hypothesis.
  • To offer new insights into replication assessment beyond traditional statistical measures.

Main Methods:

  • Utilized the design of original studies to estimate a theoretically interesting effect size.
  • Calculated a likelihood ratio to compare two models: one based on the estimated effect size and a null model.
  • Applied the developed procedure to data from the Replication Project (2015).

Main Results:

  • The likelihood-based approach was successfully developed and applied.
  • The procedure indicated that a substantial proportion of replications did not yield evidence for a theoretically interesting effect.
  • This suggests potential issues with the reproducibility of findings in the analyzed dataset.

Conclusions:

  • The developed likelihood-based method offers a valuable tool for evaluating scientific replications.
  • The findings underscore the importance of rigorous replication assessment in scientific research.
  • A significant number of studies within the Replication Project may not have successfully reproduced their original effect sizes.