Joint Research Conference

June 24-26, 2014

Split Plots Pros and Cons

This talk uses a series of case studies that illustrate thepros and cons of running factorial split-plot designs. The pros include:  Practical: Randomizing hard-to-change factors in groups rather, than randomizing every run, is much less labor and time intensive.  Malleable: Factors that naturally have large experimental units can be easily combined with factors having smaller experimental units.  More powerful: Tests for the subplot effects from the easy-to-change factors have higher power due to partitioning the variance sources.  Adaptable: New treatments can be introduced to experiments that are already in progress The cons include:  Less powerful: Tests for the hard-to-change factors are less powerful, having a larger variance to test against and fewer changes to help overcome the larger error. Unfamiliar: Analysis requires specialized methods to cope with partitioned variance sources.  Different: Hard-to-change (whole-plot) and easy-to-change (subplot) factor effects are tested against different estimated noise. This can result in large whole-plot effects not being statistically significant, whereas small subplot effects are significant even though they may not be practically important.