In sum, classical procedures employ the data to narrow down a set of hypotheses. Put in such general terms, it becomes apparent that classical procedures provide a response to the problem of induction. The data are used to get from a weak general statement about the target system to a stronger one, namely from a set of candidate hypotheses to a subset of them. The central concern in the philosophy of statistics is how we are to understand these procedures, and how we might justify them. Notice that the pattern of classical statistics resembles that of eliminative induction: in view of the data we discard some of the candidate hypotheses. Indeed classical statistics is often seen in loose association with Popper's falsificationism, but this association is somewhat misleading. In classical procedures statistical hypotheses are discarded when they render the observed sample too improbable, which of course differs from discarding hypotheses that deem the observed sample impossible.