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Essential Statistical Tests

In: Principles and Practice of Clinical Trials

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Listed:
  • Gregory R. Pond

    (McMaster University, Department of Oncology
    Ontario Institute for Cancer Research)

  • Samantha-Jo Caetano

    (McMaster University, Department of Mathematics and Statistics)

Abstract

This chapter summarizes the basic steps involved in performing commonly used statistical hypothesis tests for continuous and categorical outcomes in randomized clinical trials and further describes the statistical test procedures. Statistical hypothesis tests generally fall into two broad categories, parametric and non-parametric statistical tests. Parametric tests are statistical tests that require the assumption that the data follows some known distribution. These include t-tests, analysis of variance (ANOVA), and χ2 tests. Non-parametric tests do not require this assumption, and are robust to misspecification, but are generally more conservative. Commonly used non-parametric tests include the signed-rank test, Wilcoxon rank sum test, Kruskal-Wallis test, and Fisher’s exact test. While not an exhaustive list, this chapter will describe each of these tests in detail and give an overview of their use.

Suggested Citation

  • Gregory R. Pond & Samantha-Jo Caetano, 2022. "Essential Statistical Tests," Springer Books, in: Steven Piantadosi & Curtis L. Meinert (ed.), Principles and Practice of Clinical Trials, chapter 87, pages 1703-1716, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52636-2_118
    DOI: 10.1007/978-3-319-52636-2_118
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