Randomization is a statistical process in which a random mechanism is employed to select a sample from a population or assign subjects to different groups.[1][2][3] The process is crucial in ensuring the random allocation of experimental units or treatment protocols, thereby minimizing selection bias and enhancing the statistical validity.[4] It facilitates the objective comparison of treatment effects in experimental design, as it equates groups statistically by balancing both known and unknown factors at the outset of the study. In statistical terms, it underpins the principle of probabilistic equivalence among groups, allowing for the unbiased estimation of treatment effects and the generalizability of conclusions drawn from sample data to the broader population.[5][6]
Randomization is not haphazard; instead, a random process is a sequence of random variables describing a process whose outcomes do not follow a deterministic pattern but follow an evolution described by probability distributions. For example, a random sample of individuals from a population refers to a sample where every individual has a known probability of being sampled. This would be contrasted with nonprobability sampling, where arbitrary individuals are selected. A runs test can be used to determine whether the occurrence of a set of measured values is random.[7] Randomization is widely applied in various fields, especially in scientific research, statistical analysis, and resource allocation, to ensure fairness and validity in the outcomes.[8][9][10]
In various contexts, randomization may involve
Generating Random Permutations: This is essential in various situations, such as shuffling cards. By randomly rearranging the sequence, it ensures fairness and unpredictability in games and experiments.
Selecting Random Samples from Populations: In statistical sampling, this method is vital for obtaining representative samples. By randomly choosing a subset of individuals, biases are minimized, ensuring that the sample accurately reflects the larger population.
Random Allocation in Experimental Design: Random assignment of experimental units to treatment or control conditions is fundamental in scientific studies. This approach ensures that each unit has an equal chance of receiving any treatment, thereby reducing systematic bias and improving the reliability of experimental results.
Generating Random Numbers: The process of random number generation is central to simulations, cryptographic applications, and statistical analysis. These numbers form the basis for simulations, model testing, and secure data encryption.
Data Stream Transformation: In telecommunications, randomization is used to transform data streams. Techniques like scramblers randomize the data to prevent predictable patterns, which is crucial for securing communication channels and enhancing transmission reliability."
Randomization has many uses in gambling, political use, statistical analysis, art, cryptography, gaming and other fields.
^Oxford English Dictionary "randomization"
^Bespalov, Anton; Wicke, Karsten; Castagné, Vincent (2020), Bespalov, Anton; Michel, Martin C.; Steckler, Thomas (eds.), "Blinding and Randomization", Good Research Practice in Non-Clinical Pharmacology and Biomedicine, Handbook of Experimental Pharmacology, vol. 257, Cham: Springer International Publishing, pp. 81–100, doi:10.1007/164_2019_279, ISBN 978-3-030-33656-1, PMID 31696347, S2CID 207956615
^Kang, Minsoo; Ragan, Brian G; Park, Jae-Hyeon (2008). "Issues in Outcomes Research: An Overview of Randomization Techniques for Clinical Trials". Journal of Athletic Training. 43 (2): 215–221. doi:10.4085/1062-6050-43.2.215. ISSN 1062-6050. PMC 2267325. PMID 18345348.
^Saghaei, Mahmoud (2011). "An Overview of Randomization and Minimization Programs for Randomized Clinical Trials". Journal of Medical Signals and Sensors. 1 (1): 55–61. doi:10.4103/2228-7477.83520. ISSN 2228-7477. PMC 3317766. PMID 22606659.
^Desharnais, Josée; Laviolette, François; Zhioua, Sami (2013-06-01). "Testing probabilistic equivalence through Reinforcement Learning". Information and Computation. 227: 21–57. doi:10.1016/j.ic.2013.02.002. ISSN 0890-5401.
^Sedgwick, Philip (2011-11-23). "Random sampling versus random allocation". BMJ. 343: d7453. doi:10.1136/bmj.d7453. ISSN 0959-8138. S2CID 71545281.
^Alhakim, A; Hooper, W (2008). "A non-parametric test for several independent samples". Journal of Nonparametric Statistics. 20 (3): 253–261. CiteSeerX 10.1.1.568.6110. doi:10.1080/10485250801976741. S2CID 123493589.
^Fowler, Kathryn L.; Fleming, Martin D. (2023-01-01), Eltorai, Adam E. M.; Bakal, Jeffrey A.; Newell, Paige C.; Osband, Adena J. (eds.), "Chapter 58 - Principles and methods of randomization in research", Translational Surgery, Handbook for Designing and Conducting Clinical and Translational Research, Academic Press, pp. 353–358, ISBN 978-0-323-90300-4, retrieved 2023-12-10
^Berger, Vance W.; Bour, Louis Joseph; Carter, Kerstine; Chipman, Jonathan J.; Everett, Colin C.; Heussen, Nicole; Hewitt, Catherine; Hilgers, Ralf-Dieter; Luo, Yuqun Abigail; Renteria, Jone; Ryeznik, Yevgen; Sverdlov, Oleksandr; Uschner, Diane (2021-08-16). "A roadmap to using randomization in clinical trials". BMC Medical Research Methodology. 21 (1): 168. doi:10.1186/s12874-021-01303-z. ISSN 1471-2288. PMC 8366748. PMID 34399696.
^Toroyan, Tami; Roberts, Ian; Oakley, Ann (2000-10-01). "Randomisation and resource allocation: a missed opportunity for evaluating health care and social interventions". Journal of Medical Ethics. 26 (5): 319–322. doi:10.1136/jme.26.5.319. ISSN 0306-6800. PMC 1733281. PMID 11055032.
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