Partial replication of small two-level factorial designs

Replicating runs in designed experiments is good practice. The most important reason to replicate runs is to allow for a model-independent estimate of the error variance. Without the pure error degrees of freedom provided by replicated runs, the error variance will be biased if the fitted model is missing an active effect. This work provides a replication strategy for full-factorial designs having two to four factors. However, our approach is general and could be applied to any factorial experiment.