Experimenters' Club (prior DOE Club)

Video 1: Challenge -  Beyond One Best Model: What a DSD Can Really Tell You The Q2 session of the Experimenter’s Club featured a lively discussion on George Box’s famous reminder that "all models are wrong, but some are useful." It began with a user questioning whether unexpectedly strong interaction and quadratic effects in a 6-factor Definitive Screening Design (DSD) should be trusted. What followed was a thoughtful exchange showing that the real issue is not whether one model is “correct,” but how to interpret a design space in which many plausible models coexist. Community Question: https://community.jmp.com/t5/Discussions/quot-Surprising-quot-results-in-an-DSD-Design/td-p/941518 LinkedIn post from Victor: https://www.linkedin.com/posts/victorguiller_experimentersclub-designofexperiments-share-7453440558682894336-j7Ki?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAg2XG8BduAUlMeluDXcuMFV-P_Hxv1KA0k Video 2: Discussion The conversation highlighted that in a DSD with a limited number of runs, it is impossible to estimate every main, interaction, and quadratic effect simultaneously without structural tradeoffs. As a result, partial aliasing, multicollinearity, and model multiplicity are natural features of the problem rather than signs of failure. The discussion emphasized that different estimation methods and selection criteria, such as R², adjusted R², AICc, BIC, and RMSE, can lead to different but still defensible models. A key takeaway is that experimenters should not focus only on finding a single best-fitting model. Instead, they should compare multiple strong candidates, look for effects that appear consistently across good models, apply effect hierarchy and domain knowledge, and consider augmentation when confirmation is needed. In that sense, the design process becomes less about reacting to surprising results and more about disciplined model judgment in real-world experimental work. ... View more

Video 1: JMP's DOE platform: A new addition to the artist's toolkit Read the blog from @BillFish828 here: https://community.jmp.com/t5/JMP-Blog/JMP-s-DOE-platform-A-new-addition-to-the-artist-s-toolkit/ba-p/919860 Question: What is the difference between Taguchi design and Orthogonal array. MOLS is a balanced fractional factorial design matrix that ensures independence between factors, whereas a Taguchi Design utilizes these matrices to optimize the S-N ratio mean response enhancing robustness Video 2:  Why randomize when n=1 Comment: My point was less about the trend, it was more about a not identifiable effect, so something you really did not know before the experiment. And my recommendation is to start with as many influences you can think of, even some far fetched influences, to select the factors from, and then observe any not included factor if possible. Video 3: In-depth analysis of DOE's such as Response Surfaces (RS) ... View more

This Club is a participant-driven meeting designed to encourage attendee involvement and knowledge sharing. Join us for these lightly structured discussions to share your ideas and learn more about Design of Experiments (DOE), Bayesian Optimization, and other advanced optimization strategies in JMP. Whether you’re exploring experimental design or looking to optimize complex processes, this is the place to connect, ask questions, and learn together. What can you expect? Quarterly meetings focused on experimentation and optimization. Lightly structured discussions about DOE, Bayesian Optimization, and related techniques. Experts and novices are welcome—bring your questions, share your experiences, and learn from the community. Learning and networking opportunities to deepen your understanding of data-driven design and optimization. Please send in your questions in advance to help us get the ball rolling. --> Register here ... View more

This Club is a participant-driven meeting designed to encourage attendee involvement and knowledge sharing. Join us for these lightly structured discussions to share your ideas and learn more about Design of Experiments (DOE), Bayesian Optimization, and other advanced optimization strategies in JMP. Whether you’re exploring experimental design or looking to optimize complex processes, this is the place to connect, ask questions, and learn together. What can you expect? Quarterly meetings focused on experimentation and optimization. Lightly structured discussions about DOE, Bayesian Optimization, and related techniques. Experts and novices are welcome—bring your questions, share your experiences, and learn from the community. Learning and networking opportunities to deepen your understanding of data-driven design and optimization. Please send in your questions in advance to help us get the ball rolling. -->  Register here ... View more

Wishing all our members a joyful holiday season and a fantastic start to 2026! :party_popper: We look forward to connecting with you through more meetings and learning opportunities in the coming year: 27 Jan. - Q1 Experimenters' Club 28 Apr. - Q2 Experimenters' Club Thank you for being part of our community—here’s to learning, sharing, and growing together in 2026! ... View more