• To understand how the permutation test can be used to test null hypothesis
• To appreciate how much easier it is to understand than significance tests based on t statistics
• To illustrate how the permutation test requires fewer assumptions than t-tests
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.
Nurse Researcher. doi: 10.7748/nr.2024.e1920Peer review
This article has been subject to external double-blind peer review and checked for plagiarism using automated softwareCorrespondence
Liu XS (2024) The permutation test: a simple way to test hypotheses. Nurse Researcher. doi: 10.7748/nr.2024.e1920
Published online: 30 January 2024
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