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Randomization is the process of making something random; this can mean:

• Generating a random permutation of a sequence (such as when shuffling cards).
• Selecting a random sample of a population (important in statistical sampling).
• Generating random numbers: see Random number generation.
• Transforming a data stream using a scrambler in telecommunications.

Randomization is used extensively in the field of gambling. Imperfect randomization may allow a skilled gambler to have an advantage, so much research has been devoted to effective randomization. A classic example of randomization is shuffling playing cards.

Randomization is a core principle in the statistical theory of design of experiments. Its use was extensively promoted by R.A. Fisher in his book Statistical Methods for Research Workers. Randomization involves randomly allocating the experimental units across the treatment groups. Thus, if the experiment compares a new drug against a standard drug used as a control, the patients should be allocated to new drug or control by a random process.

Randomization is not haphazard; it serves a purpose in both frequentist and Bayesian statistics. A frequentist would say that randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design. Considerations of bias are of little concern to Bayesians, who recommend randomization because it produces ignorable designs. In design of experiments, frequentists prefer Completely Randomized Designs. Other experimental designs are used when a full randomization is not possible. These cases include experiments that involve blocking and experiments that have hard-to-change factors.

See also: Applications of randomness

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.

Non-algorithmic randomization methods include:

• Casting yarrow stalks (for the I Ching)
• Throwing dice
• Drawing straws
• Shuffling cards
• Roulette wheels
• Drawing pieces of paper or balls from a bag
• "Lottery machines"
• Observing atomic decay using a radiation counter

• RQube - Generate quasi-random stimulus sequences for experimental designs
• RandList - Randomization List Generator

• Randomized algorithm