Resampling Tests: Concept and R Applications



Parametric tests such as t-test, ANOVA, etc. requires some assumptions about the distribution of the scores in the universe. If those assumptions are not met it is a good idea to compute non-parametric tests instead of parametric tests. Traditional non-parametric tests such as Wilcoxon Sum of Ranks and Kruskal Wallis tests etc. focus on the sum of ranks and mean ranks to compare the group scores. On the other hand, resampling tests present a different point of view on this process. One of the mostly used resampling tests is the randomization test. The basic principles of randomization tests are comparing the original test statistic (t values, F values, r coefficient, etc.) to the test statistics derived from randomly generated samples. Although usage of randomization tests in the world is pervading day by day in Turkey it is very rarely used. This may be because of insufficient written source published in Turkey. Moreover, the R programming language has become very popular recently. So in this study, it is aimed to explain the computation process of randomization tests using R codes. In this study, at first, some basic concepts about randomization tests were presented. Then randomization tests were exemplified for independent samples t-test, repeated sample t-test, one-way analysis of variance (one way ANOVA) using R codes. It is hoped that this study guide and motivate researchers to use randomizations tests and r programming language in their research.


resampling tests, randomization tests, r programming lan-guage, non-parametrik tests, bootsrap,

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