randomize

Randomization is the process of making something random; this means:

• 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.

Applications

Randomization is used extensively in the field of gambling (or generally being random). 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.

Statistics

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.

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.

Randomized experimentation is not haphazard. Randomization reduces bias by equalising other factors that have not been explicitly 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.

Resampling

An important route to statistical inference is via resampling of the observed data. Alternative versions of the original data-set, that "might have been observed" are created by randomization of that data-set. Then the variation between the results obtained for these alternative data-sets is a guide to the uncertainty of statistics estimated from the original data.

See also: Applications of randomness

Techniques

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

See also

• Randomized algorithm

References

External links

• RANDI2 - Open source Randomization tool for clinical trials / research
• RQube - Generate quasi-random stimulus sequences for experimental designs
• RandList - Randomization List Generator