using DOE for OFAT experiments

[#1]

just wondering, would it be useful to use a DOE if you are doing an OFAT (one factor at a time experiment) experiment?  would you get any more statistical understanding from the DOE than if you just did the scan and a regression analysis?

[#2 · Accepted solution]

Hello @LogitPorcupine1,

If you have several factors in your system, I don't see why you wouldn't use DoE instead of OFAT approach.
DoE has many benefits compared to OFAT, here are some :

• Limited (and planned/controlled in advance !) number of experiments,
• Interactions and non-linear effects can be studied,
• Mathematical model to explain or predict response(s) variations,
• High efficiency (information vs. number of experiments),
• Errors are shared equally/homogeneously in the design space,
• Possibility to create constraints/customs designs
• Iterative process through "Augmentation" and a lot of strategies possible : you can start by screening main effects, then checking interactions, and finally build a robust Response Surface Model for predicting accurately your responses, ... 

There are a lot of ressources to learn DoE with JMP, here are some :

Design of Experiments Intro Kit | Getting Started with JMP

Statistical Thinking (STIPS) - Free Online Statistics Course | JMP

Design of Experiments (DOE) Course | JMP

Design of Experiments | JMP

You can also check this very nice article on Nature about the benefits of using DoE in order to avoid "blank spots/area" in your experimental space (Figure 2) : A Design of Experiments (DoE) Approach Accelerates the Optimization of Copper-Mediated 18F-Fluorinat...

Hope this will help your reflexion and understanding,

[#3]

Just to add to Victor's excellent list of why do multi-factor experiments vs. OFAT:  OFAT's have an extremely narrow inference space.  ALL other factors must be kept constant during OFAT.  The narrow inference space negatively affects your confidence in extrapolating the results beyond the current study.