In more than a century and a half of effort, observations of the deep sky have made remarkable contributions to our knowledge of cosmology. Today, there exists a well-measured cosmological model that fits all known data to accuracies better than 10 percent. Astonishingly, it is possible that within the next decade or so, observational errors will be reduced to the per cent level. Because cosmology is an observational, not experimental science, precision scientific inference is a matter of solving a statistical inverse problem using Markov chain Monte Carlo (MCMC) techniques. But because the forward model evaluation is so expensive -- large supercomputer codes must be run in each case to obtain predictions at better than a per cent accuracy -- it is hopeless to proceed via brute force. In this talk I will describe 'cosmic calibration', a statistical framework we have recently developed that uses sophisticated sampling design and interpolating strategies in high-dimensional spaces, combined with results from a finite set of simulations run over a range of cosmological parameters. The framework produces an accurate 'oracle' that can be run essentially instantaneously on a laptop; the oracle, or 'emulator', yields accurate results for observables at parameter values that lie anywhere within the range of the parameter sampling design. Cosmic calibration enables MCMCs to be run on laptops in tens of minutes instead of many years on supercomputers. The basic ideas can be applied to many other fields.