  What is the purpose of pre-analysis plans, and how should they be designed?
We model the interaction between an agent who analyzes data and a principal who
makes a decision based on agent reports. The agent could be the manufacturer of
a new drug, and the principal a regulator deciding whether the drug is
approved. Or the agent could be a researcher submitting a research paper, and
the principal an editor deciding whether it is published. The agent decides
which statistics to report to the principal. The principal cannot verify
whether the analyst reported selectively. Absent a pre-analysis message, if
there are conflicts of interest, then many desirable decision rules cannot be
implemented. Allowing the agent to send a message before seeing the data
increases the set of decision rules that can be implemented, and allows the
principal to leverage agent expertise. The optimal mechanisms that we
characterize require pre-analysis plans. Applying these results to hypothesis
testing, we show that optimal rejection rules pre-register a valid test, and
make worst-case assumptions about unreported statistics. Optimal tests can be
found as a solution to a linear-programming problem.
