Why you should read this article:

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To understand how the permutation test can be used to test null hypothesis

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To appreciate how much easier it is to understand than significance tests based on t statistics

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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.