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Chapter 4 - Effects: What Tests Test

from Part I - Why We Use Statistics

Published online by Cambridge University Press:  26 January 2019

Paul Cairns
Affiliation:
University of York
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Summary

Effects and the size of effects are proposed as a key way to overcome the limitations of significance testing in modern statistics. However, traditionally, effects are usually added on to the statistical testing procedure. This chapter proposes instead thinking first about what effects to look for in the context of the research and its maturity within the discipline of HCI. The main types of effect, changes in location, stochastic dominance and (co)variation, are described. Consideration of which of these effects that current best knowledge can predict leads to choosing the test most suitable to find those effects.

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Publisher: Cambridge University Press
Print publication year: 2019

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  • Effects: What Tests Test
  • Paul Cairns, University of York
  • Book: Doing Better Statistics in Human-Computer Interaction
  • Online publication: 26 January 2019
  • Chapter DOI: https://doi.org/10.1017/9781108685139.005
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  • Effects: What Tests Test
  • Paul Cairns, University of York
  • Book: Doing Better Statistics in Human-Computer Interaction
  • Online publication: 26 January 2019
  • Chapter DOI: https://doi.org/10.1017/9781108685139.005
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.

  • Effects: What Tests Test
  • Paul Cairns, University of York
  • Book: Doing Better Statistics in Human-Computer Interaction
  • Online publication: 26 January 2019
  • Chapter DOI: https://doi.org/10.1017/9781108685139.005
Available formats
×