  We discuss the simplified likelihood framework as a systematic approximation
scheme for experimental likelihoods such as those originating from LHC
experiments. We develop the simplified likelihood from the Central Limit
Theorem keeping the next-to-leading term in the large $N$ expansion to
correctly account for asymmetries. Moreover, we present an efficient method to
compute the parameters of the simplified likelihood from Monte Carlo
simulations. The approach is validated using a realistic LHC-like analysis, and
the limits of the approximation are explored. Finally, we discuss how the
simplified likelihood data can be conveniently released in the HepData error
source format and automatically built from it, making this framework a
convenient tool to transmit realistic experimental likelihoods to the
community.
