In some cases, the bell-shaped curve assumption of classical statistics is correct, and then the bootstrap method gives the same answer as the standard methods.

Many methods of statistical analysis, such as the bootstrap method, require random numbers.

As such, the bootstrap method does not violate assumptions of normality and is therefore recommended for small sample sizes.

This sampling process is repeated many times as for other bootstrap methods.

Alternative option is to use bootstrap methods to assess whether the group means are different.

Within an integrated circuit a bootstrap method is used to allow internal address and clock distribution lines to have an increased voltage swing.

The uncertainties are based on a bootstrap method as described on the Global Trends page.

One popular way of choosing the empirically optimal k in this setting is via bootstrap method.

A bootstrap method of PAUP.4.06a with 100 bootstraps was used to generate the tree.

A Dollo parsimony tree was constructed and the reliability of its topology was assessed using the bootstrap method as described above.