resampling was originally developed to help analysts determine how much their
results might have changed if another random sample had been used instead and
how different the results might be when a model is applied to new data. Bootstrapping
has also gained an increasing popularity in the field of resampling small data
Bootstrapping is based on sampling with replacement to form a calibration set.
For the most popular variant, the 0.632 bootstrap, n times a sample is selected from n samples for the calibration set whereby the same sample can
be selected several times. Then, the samples, which were not picked, are used
for the test set. The chance that a particular sample is not picked for the
calibration set is:
the test set contains about 36.8% of the samples and the calibration set about
63.2% with some samples replicated in the calibration set. Bootstrapping is
not affected by asymptotic inconsistency and might be the best way of estimating
the error for very small data sets whereby the complete procedure can be repeated
arbitrarily often .