Xiaofeng Steven Liu Professor, Department of Educational and Developmental Science, University of South Carolina, Columbia, SC, US
Why you should read this article:

• 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. 32, 2, 8-13. doi: 10.7748/nr.2024.e1920

Peer review

This article has been subject to external double-blind peer review and checked for plagiarism using automated software

Correspondence

xliu@email.sc.edu

Conflict of interest

None declared

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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