A survey of some recent developments in the bootstrap methodology is given, concentrating on basic ideas and applications rather than theoretical considerations. Topics include statistical error, double bootstrapping, bootstrapping complicated data sets, wild bootstrap, smoothed bootstrap, modified bootstrap, confidence intervals, bootstrap calibration and bootstrap-based hypothesis testing. The above topics are discussed under the assumption of independent data. A major development recently of bootstrap methods has been their application to dependent data. Topics that are discussed under this heading include the moving block bootstrap, circular block bootstrap, stationary bootstrap and the autoregressive sieve bootstrap. The problem of choosing the block length data-dependently is also addressed.