Poll 2: If you are not using optimal design, why not?
Some comments to continue the discussion online:
From @MartinNovak: I would love to hear more about gradually progressed optimal experiments/designs -> how to properly combine several subsequent groups/blocks of experiments statistically?
From Tin Huu Bui: How does JMP choose what point to test in optimal design? I'm research about a topic called Bayesian Optimization. That from each test, you will know what should you test next to gain biggest knowledge/ information. Can we consider it as a replacement for DOE?
Answer: It depends on the optimality criterion and variance in your design.
You can try to augment a design based on real data or simulation data, JMP will choose points where the prediction variance is highest.
From @ruskicar: Do space filling designs in general require more experiments than model-based designs?
Answer: Yes in general. If you do a comparative test in JMP, you'll see that optimal designs are more cost-efficient than space-filling designs. But they may not be used for the same problem,
Discussion after the Breakouts:
Session 1_Breakout Discussion.mp4
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@martindemel : we tried DOE in the past and it did not work (quote often means they did not do a statisticially designed experiment when sking for more information what they did)
@Georg: too much noise - Low power run or bigger test or reduce the expectation
@Victor_G : Yes, interactions are often underestimated
