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From Wikipedia, the free encyclopedia

Randomization is the process of making something random; in various contexts this involves, for example:

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.

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  • ✪ What does randomization mean for research volunteers?
  • ✪ Why Randomization Is Important
  • ✪ Randomization With Excel
  • ✪ Randomizing a list of students using Excel
  • ✪ 7. Randomization: Skip Lists


What does randomization mean for research volunteers? [Intro] Hello! The Federal Office for Human Research Protections, or OHRP, created these videos to help you learn more about participating in research. Deciding if you want to volunteer for a research study can be difficult, and this decision can have important consequences. Research that compares interventions or treatments commonly uses “randomization” as part of the study design, which means that volunteers are assigned randomly to particular study “arms,” or groups. Which intervention or treatment the volunteers receive depends on the study arm they are assigned to. This video provides some basic information about why researchers use randomization in studies and what randomization means to you as a potential research volunteer. [What does “random assignment” mean?] When something happens “randomly,” that means it happens completely by chance, and that no one can predict or control the result. Drawing numbers out of a hat to separate people into two teams is a random procedure. So is flipping a coin to decide who goes first in a game. Randomization is a commonly used procedure in clinical research. Research volunteers may be randomized to different arms in a study. This means that a volunteer’s assignment to a particular study arm is by chance, and that it is not planned or controlled by the researcher, the volunteer’s doctor, or anyone else. Which study arm a volunteer ends up in is random, like whether a coin flip comes up heads or tails, without any input from the study team. [Why is randomized assignment used in research?] Researchers use randomized assignment to help get reliable answers to research questions. Suppose researchers want to know if a new drug can help people fight an infection better than one already being used. They enroll volunteers who have the type of infection the drugs are supposed to treat. Then they randomly assign volunteers to one of two study arms. In one study arm, volunteers receive a drug that is currently prescribed by doctors. Volunteers in the other study arm receive the new, experimental drug. Then researchers collect information about how the volunteers in each group respond to the different drugs. If the researchers get to decide who gets which drug and don’t use randomization, they might unintentionally give people who seem sicker the new drug—perhaps because they think the new drug might work better, or maybe they would give sicker volunteers the commonly-used medicine, because they have more experience with it. But if either of those things happened, the results of the study wouldn’t tell researchers whether one drug really works better than the other because the volunteers in each arm are too different from each other. A difference in results between the two study arms might occur just because one arm includes sicker volunteers. To make sure that any differences in results between the study arms are caused only by the different drugs, the volunteer groups need to be similar in health and other characteristics. Like the saying goes, it’s important to compare apples with apples. Randomization is supposed to help make the groups more similar. When volunteers are assigned randomly to the study arms, no one controls which group a volunteer will be in. Therefore, as long as there are enough volunteers, the study arms should be similar. In our example, each study arm would have roughly the same number of volunteers with mild and serious infections, and be generally similar in other characteristics. This way, the only thing that is different between the two groups is the drug they take. The researchers can be more certain that any differences in the results are caused by the drugs being studied and not the characteristics of the volunteers in the groups. This is why randomized studies can produce more reliable results. Sometimes researchers take additional steps to avoid unintentionally influencing the results. For example, they may design the study so that volunteers won’t know, or are “blinded” to, which group they are in. Other times, both the researchers and the volunteers don’t know which group the volunteers are in. This is called a “double-blind” study. It ensures that no one can intentionally or unintentionally influence the results. Double-blind randomized studies are one of the best research designs and generally produce the most reliable results. [So what does it mean for research volunteers to be “randomized”?] If you are asked to participate in a research study with a randomized design, here’s what you need to know: • Your assignment to a particular study arm or group is done randomly, like a coin flip. The research team cannot choose which group you end up in. • Similarly, your doctor cannot choose which study arm you end up in, even if she or he thinks that one group might be better for you than the other. Your assignment to a study arm is entirely by chance. • You also cannot choose which group you are in, and you may not get the one that you want. • It is possible that the researcher, your doctor, and you will not know which study arm you are in, and won’t be allowed to find out as long as the study is still going on. • It is important to remember that, unlike medical treatment, research is not designed to specifically address your needs and interests as an individual patient. The care that you receive in a research study does not necessarily put your individual interests first, will not necessarily benefit you, and could even be harmful, even though there are protections in place. Research volunteers can help science answer specific medical or behavioral questions. Researchers hope that these answers will contribute to a better understanding of human biology and behavior, and lead to more effective medical treatments in the future. [Closing] This video was designed to answer some basic questions about randomization in research and give you some things to think about. Deciding whether to participate in research can be hard. Don’t be afraid to ask the research team for more information and talk with them about your concerns. It’s their job to give you the information you need so you can make the most informed decision about whether to participate. OHRP has created a variety of resources to help you think about research participation. For more information, check out our website at www dot hhs dot gov forward slash about dash research dash participation.



Randomization is used in statistics and in gambling.


Randomization is a core principle in statistical theory, whose importance was emphasized by Charles S. Peirce in "Illustrations of the Logic of Science" (1877–1878) and "A Theory of Probable Inference" (1883). Randomization-based inference is especially important in experimental design and in survey sampling. The first use of "randomization" listed in the Oxford English Dictionary is its use by Ronald Fisher in 1926.[1][2]

Randomized experiments

In the statistical theory of design of experiments, randomization involves randomly allocating the experimental units across the treatment groups. For example, if an experiment compares a new drug against a standard drug, then the patients should be allocated to either the new drug or to the standard drug control using randomization. Randomization reduces confounding by equalising so-called factors ( independent variables) that have not been accounted for in the experimental design.

Survey sampling

Survey sampling uses randomization, following the criticisms of previous "representative methods" by Jerzy Neyman in his 1922 report to the International Statistical Institute.


Some important methods of statistical inference use resampling from the observed data. Multiple alternative versions of the data-set that "might have been observed" are created by randomization of the original data-set, the only one observed. The variation of statistics calculated for these alternative data-sets is a guide to the uncertainty of statistics estimated from the original data.


Randomization is used extensively in the field of gambling. Because poor randomization may allow a skilled gambler to take advantage, much research has been devoted to effective randomization. A classic example of randomizing is shuffling playing cards.


Although historically "manual" randomization techniques (such as shuffling cards, drawing pieces of paper from a bag, spinning a roulette wheel) were common, nowadays automated techniques are mostly used. As both selecting random samples and random permutations can be reduced to simply selecting random numbers, random number generation methods are now most commonly used, both hardware random number generators and pseudo-random number generators.


Randomization is used in optimization to alleviate the computational burden associated to robust control techniques: a sample of values of the uncertainty parameters is randomly drawn and robustness is enforced for these values only. This approach has gained popularity by the introduction of rigorous theories that permit one to have control on the probabilistic level of robustness, see scenario optimization.

Non-algorithmic randomization methods include:

See also


  1. ^ Fisher RA. The arrangement of field experiments. J Min Agri GB 1926; 33: 700-725.
  2. ^ Oxford English Dictionary "randomization"

External links

  • RQube - Generate quasi-random stimulus sequences for experimental designs
  • RandList - Randomization List Generator
This page was last edited on 2 May 2019, at 14:26
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