SummaryThe end result of an experiment is an inference, which is typically made after the data have been seen (a post-data inference). Classical frequency theory has evolved around pre-data inferences, those that can be made in the planning stages of an experiment, before data are collected. Such pre-data inferences are often not reasonable as post-data inferences, leaving a frequentist with no inference conditional on the observed data. We review the various methodologies that have been suggested for frequentist post-data inference, and show how recent results have given us a very reasonable methodology. We also discuss how the pre-data/post-data distinction fits in with, and subsumes, the Bayesian/frequentist distinction.