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2 results

  • Chapter 2 - Testing the Null

  • from Part I - Why We Use Statistics
  • Paul Cairns, University of York
  • Book:
    Doing Better Statistics in Human-Computer Interaction
    Published online:
    26 January 2019
    Print publication:
    07 February 2019, pp 25-37

  • Summary

    Traditional statistical testing, sometimes called null hypothesis significance testing (NHST), and the use of p-values has come under strong criticism. This chapter looks at the social issues of achieving significance in statistics that has led to the problems of NHST. It also discusses, though, how the framework of severe testing provides a way to understand NHST as a means of uniting experiments, statistics and evidence for research ideas. When viewed this way, NHST can still be an important approach to data analysis in HCI.

  • 6 - A Cautionary Tail: Why You Should Not Do a One-Tailed Test

  • from Part II - How to Use Statistics
  • Paul Cairns, University of York
  • Book:
    Doing Better Statistics in Human-Computer Interaction
    Published online:
    26 January 2019
    Print publication:
    07 February 2019, pp 80-85

  • Summary

    A directional hypothesis predicts the specific way in which data are affected by an experimental manipulation. This, therefore, fits well with severe testing by making a concrete, explicit prediction. However, one-tailed testing associated with directional hypotheses regularly receives criticism because it can look like a way to make it easy to achieve statistical significance. This chapter describes why this is a problem and makes the case that despite the fit with severe testing, two-tailed testing is still the better way to do statistical analysis.