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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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Reply to Rouder (2014): good frequentist properties raise confidence.

Adam N Sanborn1, Thomas T Hills, Michael R Dougherty

  • 1Department of Psychology, University of Warwick, Coventry, CV4 7AL, UK, A.N.Sanborn@warwick.ac.uk.

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Summary
This summary is machine-generated.

Researchers should consider how statistical results are obtained, not just the final Bayes factor. Understanding the influence of stopping rules on Bayes factors enhances confidence in psychological findings.

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Area of Science:

  • Psychology
  • Statistics
  • Research Methodology

Background:

  • Established psychological findings are questioned due to the ease of achieving statistical significance, even without real effects.
  • The practice of stopping experiments based on results can artificially inflate significance.

Purpose of the Study:

  • To investigate whether researchers should solely focus on Bayes factors, irrespective of experimental stopping rules.
  • To explore the implications of stopping rules on the interpretation and reliability of Bayes factors in psychological research.

Main Methods:

  • Review of existing literature on statistical significance, Bayes factors, and experimental stopping rules.
  • Analysis of theoretical arguments and demonstrations concerning the influence of stopping rules on Bayes factors.

Main Results:

  • While Bayes factors provide correct evidence, they can be influenced by experimental stopping rules.
  • Good frequentist properties of statistical methods correlate with intuitive results and reduce future refutations.

Conclusions:

  • Researchers should remain mindful of how experimental data is collected, not just the final Bayes factor.
  • Considering frequentist properties alongside Bayes factors can bolster confidence in psychological research findings.