James Simpson

This paper describes holistic progress in answering the question of “How much testing is enough?” It covers areas in which the T&E community has made progress, areas in which progress remains elusive, and issues that have emerged since 1994 that provide additional challenges. The selected case studies used to highlight progress are especially interesting examples, rather than a comprehensive look at all programs since 1994. Suggested Citation Medlin, Rebecca, Matthew R Avery, James R Simpson, and Heather M Wojton....

Abstract This document contains the technical content for the Scientific Test and Analysis Techniques (STAT) in Test and Evaluation (T&E) continuous learning module. The module provides a basic understanding of STAT in T&E. Topics coverec include design of experiments, observational studies, survey design and analysis, and statistical analysis. It is designed as a four hour online course, suitable for inclusion in the DAU T&E certification curriculum. Slides

This document contains the technical content for the Scientific Test and Analysis Techniques (STAT) in Test and Evaluation (T&E) continuous learning module. The module provides a basic understanding of STAT in T&E. Topics covered include design of experiments, observational studies, survey design and analysis, and statistical analysis. It is designed as a four hour online course, suitable for inclusion in the DAU T&E certification curriculum. Suggested Citation Pinelis, Yevgeniya, Laura J Freeman, Heather M Wojton, Denise J Edwards, Stephanie T Lane, and James R Simpson....

Statistical literature refers to a type of error that is committed by giving the right answer to the wrong question. If a test design is adequately scoped to address an irrelevant objective, one could say that a Type III error occurs. In this paper, we focus on a specific Type III error that on some occasions test planners commit to reduce test size and resources. Suggested Citation Johnson, Thomas H., Rebecca M....

Statistical power is a useful measure for assessing the adequacy of anexperimental design prior to data collection. This paper proposes an approach referredto as the signal-to-noise transformation method (SNRx), to approximate power foreffects in a generalized linear model. The contribution of SNRx is that, with a coupleassumptions, it generates power approximations for generalized linear model effectsusing F-tests that are typically used in ANOVA for classical linear models.Additionally, SNRx follows Ohlert and Whitcomb’s unified approach for sizing aneffect, which allows for intuitive effect size definitions, and consistent estimates ofpower....

This guide provides both a general explanation of power analysis and specific guidance to successfully interface with two software packages, JMP and Design Expert (DX). Suggested Citation Freeman, Laura J., Thomas H. Johnson, and James R. Simpson. “Power Analysis Tutorial for Experimental Design Software:” Fort Belvoir, VA: Defense Technical Information Center, November 1, 2014. https://doi.org/10.21236/ADA619843. Paper:

The areas of application for design of experiments principles have evolved, mimicking the growth of U.S. industries over the last century, from agriculture to manufacturing to chemical and process industries to the services and government sectors. In addition, statistically based quality programs adopted by businesses morphed from total quality management to Six Sigma and, most recently, statistical engineering (see Hoerl and Snee 2010). The good news about these transformations is that each evolution contains more technical substance, embedding the methodologies as core competencies, and is less of a ‘‘program....