Permutation Tests and Its Bibliometric Analysis
Keywords:
Resampling, Bibliometric study, Permutation, Type I error rateAbstract
Permutation tests are successfully used in new areas of science and technology because of increasing computer power. Permutation tests along with nonparametric tests based on permutations of ranks and numerous multiple comparison procedures give attractive alternatives for standard analysis of variance and t-test. Because of its independency from the distribution, permutation tests are successful in many cases where parametric tests are not. It is the most important factor to recommend the use of permutation tests that it equalize the technical error, one of the components of error term, to zero and only treatment error remained in the error term. In this study, it was aimed to show the situation of publication dynamics of the permutation tests from 1982 to 2025. The annual percentage growth rate which was calculated as 10.6 showed that the use of permutation test will continue to increase for all the scientific areas to calculate the exact Type I error rate.
References
Anderson MJ. 2001. Permutation tests for univariate or multivariate analysis of variance and regression. Can J Fish Aquat Sci, 58: 626-639.
Anderson MJ, Robinson J. 2001. Permutation tests for linear models. Aust NZ J Stat, 43(1): 75-88.
Anderson MJ, Ter Braak CJF. 2003. Permutation tests for multi-factorial analysis of variance. J Stat Comput Simul, 73(2): 85-113.
Aria M, Cuccurullo C. 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informet, 11(4): 959-975.
Fisher RA. 1935. The Design of Experiments. Oliver and Body, Edinburgh, UK.
Hall P. 2001. Biometrika centenary: Nonparametrics. Biometrica, 88: 143-165.
Koc S, Canga D, Onem AB, Yavuz E, Sahin M. 2019. A Monte Carlo simulation study robustness of Manova test statistics in Bernoulli and Uniform distribution. BSJ Eng Sci, 2(2): 42-51.
Lin S, Lee JC. 2003. Exact test in simple growth curve models and one way ANOVA with equicorrelation error structure. J Multivar Anal, 84: 351-368.
Önder H. 2007. Using permutation tests to reduce Type I and II errors for small ruminant research. J Appl Anim Res, 32(1): 69-72.
Önder H. 2018. Nonparametric statistical methods used in biological experiments. BSJ Eng Sci, 1(1): 1-6.
Önder H, Cebeci Z. 2009. Use and comparison of permutation tests in linear models. Anadolu J Agric Sci, 24(2): 93-97.
Önder H, Cebeci Z. 2017. A review on the permutation tests. Biostat Biomet, 3(3): 555613. https://doi.org/10.19080/BBOAJ.2017.03.555613
Önder H, Tırınk C. 2022. Bibliometric analysis for genomic selection studies in animal science. J Inst Sci Technol, 12(3): 1849-1856. https://doi.org/10.21597/jist.1133397
Önder OK. 2025. Analysis of published manuscripts on Napoleon Bonaparte. BSJ Pub Soc Sci, 8(1): 1-6. https://doi.org/10.52704/bssocialscience.1541621
Özlü A. 2022. Bibliometric analysis of publications on pulmonary rehabilitation. BSJ Health Sci, 5(2): 219-225. https://doi.org/10.19127/bshealthscience.1032380
Pitman EJG. 1938. Significance tests which may be applied to samples from any population. Royal Stat Soc Supp Part I, 4(1): 119-130.
R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Black Sea Journal of Statistics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
