My plan is to review several related methods for calibrating prediction uncertainty that enjoy various properties of validity under assumptions that are standard in mainstream machine learning. The method with the simplest validity guarantees is conformal prediction, which produces prediction sets with a given bound on the probability of error. Venn prediction and conformal predictive distributions are methods of probabilistic prediction that work in the case of classification and regression, respectively; their validity guarantees are more complicated but still natural.