The debate over optional stopping is eventually concerned with the appropriate evidential impact of data. A central concern in this wider debate is the so-called likelihood principle (see Hacking 1965 and Edwards 1972). This principle has it that the likelihoods of hypotheses for the observed data completely fix the evidential impact of those data on the hypotheses. In the formulation of Berger and Wolpert (1984), the likelihood principle states that two samples \(s\) and \(s'\) are evidentially equivalent exactly when \(P_{i}(s) = kP_{i}(s')\) for all hypotheses \(h_{i}\) under consideration, given some constant \(k\). Famously, Birnbaum (1962) offers a proof of the principle from more basic assumptions. This proof relies on the assumption of conditionality.  Say that we first toss a coin, find that it lands heads, then do the experiment associated with this outcome, to record the sample \(s\). Compare this to the case where we do the experiment and find \(s\) directly, without randomly picking it. The conditionality principle states that this second sample has the same evidential impact as the first one: what we could have found, but did not find, has no impact on the evidential value of the sample. Recently, Mayo (2010) has taken issue with Birnbaum's derivation of the likelihood principle.