model discrimination design

**Model Discrimination Design** is a **DOE strategy specifically designed to distinguish between competing statistical models** — selecting experiments that maximize the expected difference between model predictions, enabling efficient determination of which model best describes the process. **How Model Discrimination Works** - **Competing Models**: Specify two or more candidate models (e.g., linear vs. quadratic, different interaction terms). - **T-Optimal**: Find design points where the predicted responses from competing models differ maximally. - **Experiments**: Run experiments at the discriminating points. - **Selection**: Use model comparison criteria (AIC, BIC, F-test) to select the best model. **Why It Matters** - **Efficient Resolution**: Resolves model ambiguity with minimum additional experiments. - **Model Selection**: Critical when data from an initial experiment doesn't clearly distinguish between models. - **Sequential**: Often used as a follow-up to an initial response surface experiment. **Model Discrimination Design** is **letting the data choose the model** — designing experiments specifically to reveal which mathematical model truly describes the process.