Frequently Asked Questions

Design of Experiments (DoE) is a structured approach to planning and analyzing experiments that maximizes information from the fewest possible trials. Instead of changing one factor at a time (which can be inefficient and miss important interactions), DoE uses statistical methods to study multiple variables simultaneously. This makes your experiments more efficient, reveals interactions between factors, and helps you extract maximum insight from limited data. It's especially valuable when experiments are expensive, time-consuming, or when you need to optimize multiple factors at once.

While traditional DoE plans all experiments upfront, Bayesian Optimization is an iterative, adaptive approach that learns from each experiment to suggest the next most promising test. It builds a statistical model from your existing data and uses it to predict where the optimum might be, then suggests experiments that balance exploration (learning about uncertain areas) and exploitation (testing near promising regions). This makes it particularly powerful when experiments are expensive or time-consuming, as it typically finds optimal conditions with fewer total trials than traditional methods.

Start with our DoE Basics guide which walks through the fundamentals. The key is to begin with simple screening experiments to identify which factors actually matter, then progressively refine your approach. Don't try to optimize everything at once - follow the four-step process: explore boundaries, screen for important factors, refine with factorial designs, and finally optimize. Most importantly, start small and build your understanding through hands-on experience. We also offer specialized courses. Feel free to contact us to explore our training options.

For traditional DoE, popular options include JMP, Design-Expert, Minitab, or free alternatives like R (with packages like DoE.base) and Python (scikit-learn, pyDOE3). For Bayesian Optimization, we recommend BayBE (free, open-source). The choice depends on your budget, programming experience, and specific needs. Many of our tutorials include practical examples with these tools to help you get started.

Absolutely! While our examples often come from chemistry and materials science, DoE and Bayesian Optimization principles apply across industries - from manufacturing and pharmaceuticals to software development and marketing. The key concepts (controlling variables, measuring responses, optimizing multiple objectives) are universal. The specific factors and responses will be different in your field, but the fundamental approach remains the same. Contact us if you'd like to discuss how to adapt these methods to your specific application.

This is a common challenge in real-world experiments. DoE handles this through blocking, randomization, and proper experimental design. If you can't control a factor but can measure it, treat it as a covariate in your analysis. If factors vary unpredictably, use randomization and replication to minimize their impact. For factors that change over time (like ambient temperature), consider blocking your experiments by time periods. The key is to acknowledge these uncontrolled factors in your design rather than ignore them.