Some will find this dependence unacceptable: the intentions and plans of the researcher are irrelevant to the evidential value of the data. But others argue that it is just right. They maintain that the impact of data on the hypotheses should depend on the stopping rule or protocol that is followed in obtaining it, and not only on the likelihoods that the hypotheses have for those data (e.g. Mayo 1996). The motivating intuition is that upholding the irrelevance of the stopping rule makes it impossible to ban opportunistic choices in data collection. In fact, defenders of classical statistics turn the table on those who maintain that optional stopping is irrelevant.  They submit that it opens up the possibility of reasoning to a foregone conclusion by, for example, persistent experimentation: we might decide to cease experimentation only if the preferred result is reached. However, as shown in Kadane et al. (1996) and further discussed in Steele (2012), persistent experimentation is not guaranteed to be effective, as long as we make sure to use the correct, in this case Bayesian, procedures.