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BOOTSTRAP [()] This is a form of RANDOMISATION TEST which is one of the alternatives to EXHAUSTIVE RE-RANDOMISATION. The BOOTSTRAP scheme involves generating subsets of the data on the basis of random sampling with replacements as the data are sampled. Such resampling provides that each datum is equally represented in the randomisation scheme; however, the BOOTSTRAP procedure has features which distinguish it from the procedure of a MONTE-CARLO TEST. The distinguishing features of the BOOTSTRAP procedure are concerned with over-sampling - there is no constraint upon the number of times that a datum may be represented in generating a single resampling subset; the size of the resampling subsets may be fixed arbitrarily independently of the parameter values of the EXPERIMENTAL DESIGN and may even exceed the total number of data. The positive motive for BOOTSTRAP resampling is the general relative ease of devising an appropriate resampling ALGORITHM(1) when the EXPERIMENTAL DESIGN is novel or complex. A negative aspect of the BOOTSTRAP is that the form of the resampling distribution with prolonged resampling converges to a form which depends not only upon the data and the TEST STATISTIC, but also upon the BOOTSTRAP resampling subset size - thus the resampling distribution should not be expected to converge to the GOLD STANDARD(1) form of the EXACT TEST as is the case for MONTE-CARLO resampling. An effective necessity for the BOOTSTRAP procedure is a source of random codes or an effective PSEUDO-RANDOM generator.
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