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Testing Simple Hypotheses

  • Robert L. Wolpert
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

Pre-experimental Frequentist error probabilities do not summarize adequately the strength of evidence from data. The Conditional Frequentist paradigm overcomes this problem by selecting a “neutral” statistic S to reflect the strength of the evidence and reporting a conditional error probability, given the observed value of S. We introduce a neutral statistic S that makes the Conditional Frequentist error reports identical to Bayesian posterior probabilities of the hypotheses. In symmetrical cases we can show this strategy to be optimal from the Frequentist perspective. A Conditional Frequentist who uses such a strategy can exploit the consistency of the method with the Likelihood Principle — for example, the validity of sequential hypothesis tests even if the stopping rule is informative or is incompletely specified.

Keywords

Error Probability Bayesian Posterior Probability Error Report Simple Hypothesis Likelihood Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • Robert L. Wolpert
    • 1
  1. 1.Institute of Statistics and Decision SciencesDuke UniversityDurhamUSA

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