On randomization, modeling and experimental design; a new example in an old discussion

Fisher and "Student" quarreled in the early days of statistics about the design of experiments, meant to measure the difference in yield between to breeds of corn. This discussion comes down to randomization versus model building. More than half a century has passed since, but the different views remain. In this paper the discussion is put in terms of artificial randomization and natural randomization, the latter being what remains after appropriate modeling. Also the Bayesian position is discussed. An example in terms of the old corn-breeding discussion is given, showing that a simple robust model may lead to inference and experimental design that outperforms the inference from randomized experiments by far. Finally similar possibilities are suggested in statistical auditing.