Considering the risk of ignoring active factors in industrial experiments

KEY POINT

This article draws attention to the importance of considering type II error in Design of Experiments contexts and shows, through two examples, an easy way to do that.

Additional information

Notes on contributors

Pere Grima

Pere Grima is Associate Professor of Statistics at the Universitat Politècnica de Catalunya – BarcelonaTech, where he also earned his PhD. One of the areas he specializes in is experimental design. He has more than twenty years of experience in helping companies implement statistical methods for quality control and improvement. He has been an advisor for the European Quality Award and has acted as a consultant to several multinational companies in Six Sigma projects.

Lourdes Rodero

Lourdes Rodero has a degree and a PhD in Statistics. She is an Associate Professor in the Department of Statistics and Operations Research at the Universitat Politècnica de Catalunya – BarcelonaTech. Her research is focused on Applied Statistics and Modeling for quality improvement using Six Sigma Methodology. She is also interested in Bayesian statistics and its use in design of experiments.

Xavier Tort-Martorell

Xavier Tort-Martorell is Professor of Statistics at the Universitat Politècnica de Catalunya – BarcelonaTech. His research interests and industrial consulting activities cover quality management and improvement, Six Sigma, design of experiments and data-based decision making. He is currently an Associate Editor at TQM&BE (Total Quality Management & Business Excellence), QTQM (Quality Technology and Quantitative Management) and the International Journal of Quality and Service Sciences. Has been an Assessor for the European Quality Award, a jury member for the Catalan Quality Prize and President of the European Network for Business and Industrial Statistics (ENBIS) in 2012 and 2013.