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20 - Multiple Testing

from Part III - Elements of Statistical Inference

Published online by Cambridge University Press:  22 July 2022

Ery Arias-Castro
Affiliation:
University of California, San Diego
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Summary

In a wide range of real-life situations, not one but several, even many hypotheses are to be tested, and not accounting for multiple inference can lead to a grossly incorrect analysis. In this chapter we look closely at this important issue, describing some pitfalls and presenting remedies that `correct’ for this multiplicity. Combination tests assess whether there is evidence against any of the null hypotheses being tested. Other procedures aim instead at identifying the null hypotheses that are not congruent with the data while controlling some notion of error rate.

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Chapter
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Principles of Statistical Analysis
Learning from Randomized Experiments
, pp. 309 - 328
Publisher: Cambridge University Press
Print publication year: 2022

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  • Multiple Testing
  • Ery Arias-Castro, University of California, San Diego
  • Book: Principles of Statistical Analysis
  • Online publication: 22 July 2022
  • Chapter DOI: https://doi.org/10.1017/9781108779197.025
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  • Multiple Testing
  • Ery Arias-Castro, University of California, San Diego
  • Book: Principles of Statistical Analysis
  • Online publication: 22 July 2022
  • Chapter DOI: https://doi.org/10.1017/9781108779197.025
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Multiple Testing
  • Ery Arias-Castro, University of California, San Diego
  • Book: Principles of Statistical Analysis
  • Online publication: 22 July 2022
  • Chapter DOI: https://doi.org/10.1017/9781108779197.025
Available formats
×