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Why optional stopping can be a problem for Bayesians.

Rianne de Heide1,2, Peter D Grünwald3,4

  • 1Leiden University, Leiden, Amsterdam, The Netherlands.

Psychonomic Bulletin & Review
|November 19, 2020
PubMed
Summary
This summary is machine-generated.

Optional stopping is not always unproblematic for Bayesian methods, especially when using default priors in Bayes factor hypothesis testing. The type of prior significantly influences the severity of issues, with some posing severe problems.

Keywords:
Bayesian statisticsHypothesis testingModel selectionStatistical inference

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Area of Science:

  • Psychology
  • Statistics
  • Bayesian Inference

Background:

  • Optional stopping, the practice of terminating data collection based on emerging results, is debated within the Bayesian psychology community.
  • Previous work suggested optional stopping poses no problem for Bayesian analyses.

Purpose of the Study:

  • To investigate the circumstances under which optional stopping is problematic for Bayesian methods.
  • To extend previous experimental findings on optional stopping and Bayesian inference.

Main Methods:

  • Slightly varying and extending experiments by Rouder (2014).
  • Examining the impact of default or pragmatic priors on the resilience to optional stopping in Bayes factor hypothesis testing.

Main Results:

  • Resilience to optional stopping can break down when default priors are used, common in practical Bayes factor hypothesis testing.
  • Three types of default priors (type 0, I, and II) were distinguished, each with varying degrees of issues regarding optional stopping.

Conclusions:

  • Optional stopping can be problematic for Bayesian methods, particularly with default priors.
  • The severity of the problem depends on the specific type of default prior employed.