But this leap is problematic when the population is ill-defined. Classical procedures require distributional assumptions, which are usually met by a large sample size. To be specific, permutation tests still assume equal variances as classical tests. Given a very large sample size, one can reject virtually any null hypothesis. Comparison across the results from different subsets of data could help the researcher to evaluate the stability of the statistics.
Replications are essential to certain classical procedures such as multiple regression.
The problem is that beta weights and R-square values are sample dependent due to the least square criterion being applied to a specific sample of data. It is important for the data analyst to cross-validate the result with other samples. You use the same numbers over and over again until you get an answer that you can't get any other way. Every theory and procedure is built on certain assumptions and requires a leap of faith to some degree. Indeed, classical statistics framework requires more assumptions than resampling does.