  We show that adding noise to raw data before making data public is effective
at screening $p$-hacked findings: spurious explanations of the outcome variable
produced by attempting multiple econometric specifications. Noise creates
"baits" that attract $p$-hackers who are uninformed about the true cause behind
the outcome but engage in data mining, inducing them to report verifiably wrong
results. On the other hand, noise only minimally impacts honest researchers who
use data to test an ex-ante hypothesis about the causal mechanism. We
characterize the optimal level of dissemination noise and highlight the
relevant tradeoffs in a simple theoretical model. Dissemination noise is a tool
already in use by statistical agencies (e.g., US Census Bureau) to protect
privacy, and we show this existing practice can be repurposed to improve
research credibility.
