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Another Look at the Lady Tasting Tea and Differences Between Permutation Tests and Randomisation Tests

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  • Jesse Hemerik
  • Jelle J. Goeman

Abstract

The statistical literature is known to be inconsistent in the use of the terms ‘permutation test’ and ‘randomisation test’. Several authors successfully argue that these terms should be used to refer to two distinct classes of tests and that there are major conceptual differences between these classes. The present paper explains an important difference in mathematical reasoning between these classes: a permutation test fundamentally requires that the set of permutations has a group structure, in the algebraic sense; the reasoning behind a randomisation test is not based on such a group structure, and it is possible to use an experimental design that does not correspond to a group. In particular, we can use a randomisation scheme where the number of possible treatment patterns is larger than in standard experimental designs. This leads to exact p values of improved resolution, providing increased power for very small significance levels, at the cost of decreased power for larger significance levels. We discuss applications in randomised trials and elsewhere. Further, we explain that Fisher's famous Lady Tasting Tea experiment, which is commonly referred to as the first permutation test, is in fact a randomisation test. This distinction is important to avoid confusion and invalid tests.

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  • Jesse Hemerik & Jelle J. Goeman, 2021. "Another Look at the Lady Tasting Tea and Differences Between Permutation Tests and Randomisation Tests," International Statistical Review, International Statistical Institute, vol. 89(2), pages 367-381, August.
  • Handle: RePEc:bla:istatr:v:89:y:2021:i:2:p:367-381
    DOI: 10.1111/insr.12431
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    References listed on IDEAS

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    1. Jesse Hemerik & Jelle J. Goeman, 2018. "False discovery proportion estimation by permutations: confidence for significance analysis of microarrays," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(1), pages 137-155, January.
    2. Phipson Belinda & Smyth Gordon K, 2010. "Permutation P-values Should Never Be Zero: Calculating Exact P-values When Permutations Are Randomly Drawn," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-16, October.
    3. Jesse Hemerik & Jelle Goeman, 2018. "Exact testing with random permutations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 811-825, December.
    4. D. R. Cox, 2009. "Randomization in the Design of Experiments," International Statistical Review, International Statistical Institute, vol. 77(3), pages 415-429, December.
    5. Fortunato Pesarin, 2015. "Some Elementary Theory of Permutation Tests," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(22), pages 4880-4892, November.
    6. Braun T.M. & Feng Z., 2001. "Optimal Permutation Tests for the Analysis of Group Randomized Trials," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1424-1432, December.
    7. Aldo Solari & Livio Finos & Jelle J. Goeman, 2014. "Rotation-based multiple testing in the multivariate linear model," Biometrics, The International Biometric Society, vol. 70(4), pages 954-961, December.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    9. Nicolai Meinshausen & Peter Buhlmann, 2005. "Lower bounds for the number of false null hypotheses for multiple testing of associations under general dependence structures," Biometrika, Biometrika Trust, vol. 92(4), pages 893-907, December.
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    Cited by:

    1. Aaditya Ramdas & Rina Foygel Barber & Emmanuel J. Candès & Ryan J. Tibshirani, 2023. "Permutation Tests Using Arbitrary Permutation Distributions," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1156-1177, August.
    2. Angel G. Angelov & Magnus Ekström, 2023. "Tests of stochastic dominance with repeated measurements data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 443-467, September.

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