Give Every Experiment a Decision Rule—and a Closing Review

An experiment is easier to learn from when its decision rule is set before the results arrive—and its closeout is treated as part of the work.

Before starting, agree with people affected on the question, what evidence could change the plan, who owns the decision, and when to commit, adapt, or stop. Afterward, sort the outputs into three groups: keep, change, or release. For anything kept, name its limitation and a willing owner. Record why the trial ended, without assuming that stopping means a relaunch is due.

This combines the decision checkpoints in “When Does Experimenting Become a Way to Avoid Deciding?” by Yasmin Muhamad Hamad with the closeout practices in “A failed AI experiment can leave useful parts without needing a relaunch” by Hamza Waseem Naser. Together, they make the experiment accountable from its first question to its final handoff.