Communicating Scientific Uncertainty via Approximate Posteriors
Isaiah Andrews and
Jesse Shapiro
No 32038, NBER Working Papers from National Bureau of Economic Research, Inc
Abstract:
We cast the problem of communicating scientific uncertainty as one of reporting a posterior distribution on an unknown parameter to an audience of Bayesian decision-makers. We establish novel bounds on the audience’s regret when the analyst reports an approximation to a posterior that the audience treats as exact. Under a palatable restriction on the audience’s decision problems, the bounds take an especially convenient form. Under a further restriction on the audience’s priors, a bootstrap distribution can be used as a stand-in posterior. We propose a practical recipe for checking whether a conventional statistical report (say, a normal parameterized by a point estimate and standard error) is a good approximation, and for improving the report if it is not. We illustrate our proposals using the articles in the 2021 American Economic Review that use a bootstrap for inference.
JEL-codes: C18 C44 D81 (search for similar items in EconPapers)
Date: 2024-01
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