ENBIS-26 Conference

Session

Teaching, Consulting and Knowledge Transfer in Statistics

Sep 8, 2026, 10:55 AM

Conveners

Teaching, Consulting and Knowledge Transfer in Statistics

• Shirley Coleman (NU Solve, Newcastle University)

Teaching, Consulting and Knowledge Transfer in Statistics

• Marina Vives-Mestres (Universitat de Girona)

94. Bayesian Optimization as Design of Experiments: The Entropy Connection

Arno Strouwen (Strouwen Statistics; PumasAI; KULeuven)

9/8/26, 10:55 AM

Teaching, Consulting and Knowledge Transfer in Statistics

Bayesian optimization (BO) and classical design of experiments (DOE) are rarely taught together. DOE courses cover factorial designs, response surface methodology, and optimality criteria like D and I-optimality. BO courses cover Gaussian processes and acquisition functions like expected improvement. The two communities use different notation, different software, and publish in different...

104. A Practical Roadmap for Choosing Correct Statistical Tests, Based on Three Decades of Teaching Experience

Prof. Hossein Bevrani (University of Kurdistan)

9/8/26, 11:15 AM

Teaching, Consulting and Knowledge Transfer in Statistics

Throughout my years of research and university teaching, as well as advising master's and doctoral theses in applied fields such as economics, management, biology, geology, and agriculture, I have noticed that students and researchers often face difficulties in selecting appropriate statistical methods to validate their hypotheses. They may either choose an inappropriate method or fail to...

146. Plackett and Burman arrays Revisited

Dr Jonathan Smyth-Renshaw (Jonathan Smyth-Renshaw & Associates Ltd)

9/8/26, 11:35 AM

Teaching, Consulting and Knowledge Transfer in Statistics

Plackett-Burman designs are experimental designs presented in 1946 by Robin L. Plackett and J. P. Burman while working in the British Ministry of Supply.Their goal was to find experimental designs for investigating the dependence of some measured quantity on a number of independent variables (factors), each taking L levels, in such a way as to minimize the variance of the estimates of these...

13. CROSSVALI: A Method for Prompting AI for Data Analysis

Anne Driscoll (Virginia Tech), Dr Jennifer Van Mullekom (Virginia Polytechnic Institute & State University)

9/8/26, 12:00 PM

Teaching, Consulting and Knowledge Transfer in Statistics

Over the past few years, we have been interested in answering the question "Can ChatGPT Think Like a Statistician?" The answer is "Yes, but...". Our data analysis prompting experience has prompted us (pun intended!) to create a framework for obtaining appropriate data analyses from artificial intelligence called CROSSVALI (Context, Refined Questions, Options, Specificity, Scrutiny, Verify &...

67. Bridging the Gap: Interactive Discovery as a Booster for Preparing Industry-Ready Graduates

Paolo Chiappa (JMP), Volker Kraft (JMP)

9/8/26, 12:20 PM

Teaching, Consulting and Knowledge Transfer in Statistics

The GAISE recommendations (2025) are shaping best practices in teaching statistics, shifting the focus toward statistical thinking as a holistic investigative process. Despite related academic advancements, a persistent skills gap remains in preparing graduates for the complex unstructured problem-solving requirements of modern industry. This session proposes a framework where teaching...

148. Deep Adaptive Design for Model-Based DOE, Screening, and Bayesian Optimization

Arno Strouwen (Strouwen Statistics; PumasAI; KULeuven)

9/8/26, 12:40 PM

Teaching, Consulting and Knowledge Transfer in Statistics

Sequential experiments that adapt to incoming data are more efficient than static designs, but the required posterior inference and design optimization between steps are usually too expensive to run online. Deep Adaptive Design [DAD, Foster et al., 2021] sidesteps this by training a neural network policy onine that maps any experimental history to the next design point in a single forward...