AbstractMaximizing data information requires careful selection, termeddesign, of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning. © 2012 Wiley Periodicals, Inc.This article is categorized under:Application Areas > Science and TechnologyFundamental Concepts of Data and Knowledge > Key Design Issues in Data MiningAlgorithmic Development > Statistics