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Essential Statistical Tests

  • Gregory R. PondEmail author
  • Samantha-Jo Caetano
Living reference work entry

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.

Keywords

Hypothesis test Parametric Non-parametric T-test Chi-square ANOVA 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of OncologyMcMaster UniversityHamiltonCanada
  2. 2.Department of Mathematics and StatisticsMcMaster UniversityHamiltonCanada
  3. 3.Ontario Institute for Cancer ResearchTorontoCanada

Section editors and affiliations

  • Stephen George
    • 1
  1. 1.Dept. of Biostatistics and Bioinformatics,Basic Science DivisonDuke University, School of MedicineDurhamUSA

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