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.
We show that adding noise to 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 engage in data mining with no prior information about the true cause behind the outcome , inducing them to report verifiably wrong results. But , 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 that statistical agencies (e.g., the US Census Bureau) currently use to protect privacy, and we show this existing practice can be repurposed to improve research credibility.
D
raw
R
who are uninformed
, who engage in data mining with no prior information
D
but engage in data mining
R
On the other hand
But
R
already in use by
that
A
the
A
currently use