In experimentation, a stopping rule indicates when to stop collecting data because sufficient data have been collected. For example, for a [[Negative Binomial distribution]], the experiment is stopped after $r$ successes have been observed. Under a frequentist paradigm, [[inference]] is sensitive to stopping rules. Under a [[Bayesian]] paradigm, inference is not sensitive to stopping rules.