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2 results

  • 6 - Hypothesis Testing

  • Roberto R. Heredia, Texas A & M University International, Richard D. Hartley, University of Texas at San Antonio
  • Book:
    Social Behavioral Statistics
    Published online:
    13 January 2025
    Print publication:
    28 November 2024, pp 116-148

  • Summary

    Chapter 6 introduces the hypothesis-testing process and relevance of standard error in reaching statistical conclusions about whether to accept or reject the null hypothesis using the z-test statistic. Type I and Type II errors, along with the types of statistical tests researchers apply in testing hypotheses, are presented; these include one-tailed (directional) versus two-tailed (nondirectional) tests. Three important decision rules are the sampling distribution of means, the level of significance, and critical regions. Type I and Type II errors influence the decisions we make about our predictions of relationships between variables. Statistical decision-making is never error-free, but we have some control in reducing these types of errors.

  • 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.