Krushke (DBDA Blog Post) argues for a stopping rule based on precision. While I see his points, I think a full decision theoretic treatment including preposterior analysis might be more generally applicable. I anticipate following up on this post in the near future, but for now, here is the start of Dr. Krushke’s thought-provoking post:
Optional stopping in data collection: p values, Bayes factors, credible intervals, precision
This post argues that data collection should stop when a desired degree of precision is achieved (as measured by a Bayesian credible interval), not when a critical p value is achieved, not when a critical Bayes factor is achieved, and not even when a Bayesian highest density interval (HDI) excludes a region of practical equivalence (ROPE) to the null value.
