Multiple testing refers to any instance that involves the simultaneous testing of more than one hypothesis. If decisions about the individual hypotheses are based on the unadjusted marginal p-values, then there is typically a large probability that some of the true null hypotheses will be rejected. Unfortunately, such a course of action is still common. In this article, we describe the problem of multiple testing more formally and discuss methods which account for the multiplicity issue. In particular, recent developments based on resampling result in an improved ability to reject false hypotheses compared to classical methods such as Bonferroni.