Optimal experimental design that targets meaningful information

Abstract

Computer generation of experimental designs, for reasons including flexibility, speed, and ease of access, is the first line of approach for many experimentalists. The algorithms generating designs in many popular software packages employ optimality functions to measure design effectiveness. These optimality functions make implicit assumptions about the goals of the experiment that are not always considered and which may be inappropriate as the basis for design selection. General weighted optimality criteria address this problem by tailoring design selection to a practitioner's research questions. Implementation of weighted criteria in some popular design software is easily accomplished. The technique is demonstrated for factorial designs and for designing experiments with a control treatment. WIREs Comput Stat 2017, 9:e1393. doi: 10.1002/wics.1393 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery