Morgan, Leech, Gloeckner, & Barrett (2013). Comparing Two Groups with t Tests and Similar Nonparametric Tests (Chapter 10).

“The top right side of Table 10.1 distinguishes between between-groups and within-subjects designs. This helps determine the specific statistic to use. The other determinant of which statistic to use has do with statistical assumptions. If the assumptions are not markedly violated, you can use a parametric test. If the assumptions are markedly violated, one can use a nonparametric test, which does not have the same assumptions, as indicated by the left side of Table 10.1.” (p 171)

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Morgan, Leech, Gloeckner, & Barrett (2013). Selecting and Interpreting Inferential Statistics (Chapter 6).

“Although we recognize that our distinction between difference and associational parametric statistics is a simplification, we think it is useful because it corresponds to a distinction made by most researchers when they are conceptualizing their studies.” (p 99)

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Gay, Mills, & Airasian (2009). Educational research: Competencies for analysis and applications (9th edition).

“Nonparametric tests are not as powerful as parametric tests. In other words, it is more difficult with a nonparametric test to reject a null hypothesis at a given level of significance; usually, a larger sample size is needed to reach the same level of significance as in a parametric test.” (p 335)

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