  We show that adding noise before publishing data effectively screens
$p$-hacked findings: spurious explanations produced by fitting many statistical
models (data mining). Noise creates "baits" that affect two types of
researchers differently. Uninformed $p$-hackers, who are fully ignorant of the
true mechanism and engage in data mining, often fall for baits. Informed
researchers, who start with an ex-ante hypothesis, are minimally affected. We
show that as the number of observations grows large, dissemination noise
asymptotically achieves optimal screening. In a tractable special case where
the informed researchers' theory can identify the true causal mechanism with
very little data, we characterize the optimal level of dissemination noise and
highlight the relevant trade-offs. Dissemination noise is a tool that
statistical agencies currently use to protect privacy. We argue this existing
practice can be repurposed to screen $p$-hackers and thus improve research
credibility.
