  We consider the disclosure problem of a sender with a large data set of hard
evidence who wants to persuade a receiver to take higher actions. Because the
receiver will make inferences based on the distribution of the data they see,
the sender has an incentive to drop observations to mimic the distributions
that would be observed under better states. We predict which observations the
sender discloses using a model that approximates large datasets with a
continuum of data. It is optimal for the sender to play an imitation strategy,
under which they submit evidence that imitates the natural distribution under
some more desirable target state. We characterize the partial-pooling outcomes
under these imitation strategies, and show that they are supported by data on
the outcomes that maximally distinguish higher states. Relative to full
information, the equilibrium with voluntary disclosure reduces the welfare of
senders with little data or a favorable state, who fully disclose their data
but suffer the receiver's skepticism, and benefits senders with access to large
datasets, who can profitably drop observations under low states.
