Background: Quantitative researchers can use permutation tests to conduct null hypothesis significance testing without resorting to complicated distribution theory. A permutation test can reach conclusions in hypothesis testing that are the same as those of better-known tests such as the t-test but is much easier to understand and implement.
Aim: To introduce and explain permutation tests using two real examples of independent and dependent t-tests and their corresponding permutation tests.
Discussion: This article traces the history of permutation tests, explains the possible reason for their absence in textbooks and offers a simple example of their implementation. It provides simple code written in the R programming language to generate the null distributions and P -values for the permutation tests.
Conclusion: Permutation tests do not require the strict model assumptions of t -tests and can be robust alternatives.
Implications For Practice: Permutation tests are a useful addition to practitioners' research repertoire for testing hypotheses.
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| http://dx.doi.org/10.7748/nr.2024.e1920 | DOI Listing |
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