Nonparametric Tests
Nonparametric Tests
Nonparametric tests are frequently used in circumstances when the distribution is not normal, the distribution is unknown, or the sample size is insufficient to assume a normal distribution. Additionally, nonparametric tests should be utilized when there are extreme values or values that are obviously “out of range” (Knapp, 1998).
- Wilcoxon Rank Sum Test
The Wilcoxon rank sum test is a nonparametric test that can be used to see if the distributions of data obtained from two different groups on the same dependent variable are systematically different. This test is frequently referred to as the non-parametric equivalent of the two-sample t-test because it does not assume known distributions and does not deal with parameters. It examines if one variable has higher values than the other in two independent samples without defining directionality.
The test is non-parametric, which means it makes no assumptions about the distribution of scores. However, various assumptions are made when using this test, such as the sample taken from the population is random, that there is independence within the samples and mutual independence, and that an ordinal measurement scale is used (du Prel et al., 2010).
References
du Prel, J. B., Röhrig, B., Hommel, G., & Blettner, M. (2010). Choosing statistical tests: part 12 of a series on evaluation of scientific publications. Deutsches Arzteblatt international, 107(19), 343–348. https://doi.org/10.3238/arztebl.2010.0343
Knapp, T. (1998). Quantitative nursing research. Sage Publications.
SPSS tutorials: Independent sample t test. (2022, February 9). Kent State University. Retrieved March 1, 2022, from https://libguides.library.kent.edu/SPSS/IndependentTTest
Xu, M., Fralick, D., Zheng, J. Z., Wang, B., Tu, X. M., & Feng, C. (2017). The Differences and Similarities Between Two-Sample T-Test and Pa
