Kelly Avery

This training provides details regarding the use of design of experiments, from choosing proper response variables, to identifying factors that could affect such responses, to determining the amount of data necessary to collect. The training also explains the benefits of using a Design of Experiments approach to testing and provides an overview of commonly used designs (e.g., factorial, optimal, and space-filling). The briefing illustrates the concepts discussed using several case studies....

This is a briefing intended to motivate and explain the basic concepts of applying statistics to verification and validation. The briefing will be presented at the Navy M&S VV&A WG (Sub-WG on Validation Statistical Method Selection). Suggested Citation Pagan-Rivera, Keyla, John T Haman, Kelly M Avery, and Curtis G Miller. Statistical Methods for M&S V&V: An Intro for Non- Statisticians. IDA Product ID-3000770. Alexandria, VA: Institute for Defense Analyses, 2024....

This briefing presented at Dataworks 2022 provides examples of potential ways in which Design of Experiments (DOE) could be applied to initially scope cyber assessments and, based on the results of those assessments, subsequently design in greater detail cyber tests. Suggested Citation Gilmore, James M, Kelly M Avery, Matthew R Girardi, and Rebecca M Medlin. Thoughts on Applying Design of Experiments (DOE) to Cyber Testing. IDA Document NS D-33023. Alexandria, VA: Institute for Defense Analyses, 2022....

This document presents arguments and methods for using space-filling designs (SFDs) to plan modeling and simulation (M&S) data collection. Suggested Citation Avery, Kelly, John T Haman, Thomas Johnson, Curtis Miller, Dhruv Patel, and Han Yi. Test Design Challenges in Defense Testing. IDA Product ID 3002855. Alexandria, VA: Institute for Defense Analyses, 2024. Slides: Paper:

Advances in computational power have allowed both greater fidelity and more extensive use of such models. Numerous complex military systems have a corresponding model that simulates its performance in the field. In response, the DoD needs defensible practices for validating these models. Design of Experiments and statistical analysis techniques are the foundational building blocks for validating the use of computer models and quantifying uncertainty in that validation. Recent developments in uncertainty quantification have the potential to benefit the DoD in using modeling and simulation to inform operational evaluations....

This handbook focuses on methods for data-driven validation to supplement the vast existing literature for Verification, Validation, and Accreditation (VV&A) and the emerging references on uncertainty quantification (UQ). The goal of this handbook is to aid the test and evaluation (T&E) community in developing test strategies that support model validation (both external validation and parametric analysis) and statistical UQ. Suggested Citation Wojton, Heather, Kelly M Avery, Laura J Freeman, Samuel H Parry, Gregory S Whittier, Thomas H Johnson, and Andrew C Flack....

An operational test is resource-limited and must therefore rely on both live test data and modeling and simulation (M&S) data to inform a full evaluation. For the Joint Air-to-Ground Missile (JAGM) system, we needed to create a test design that accomplished dual goals, characterizing missile performance across the operational space and supporting rigorous validation of the M&S. Our key question is which statistical techniques should be used to compare the M&S to the live data?...

In the test community, we frequently use statistics to extract meaning from data. These inferences may be drawn with respect to topics ranging from system performance to human factors. In this mini-tutorial, we will begin by discussing the use of descriptive and inferential statistics. We will continue by discussing commonly used parametric and nonparametric statistics within the defense community, ranging from comparisons of distributions to comparisons of means. We will conclude with a brief discussion of how to present your statistical findings graphically for maximum impact....

In the operational testing of DoD weapons systems, modeling and simulation (M&S) is often used to supplement live test data in order to support a more complete and rigorous evaluation. Before the output of the M&S is included in reports to decision makers, it must first be thoroughly verified and validated to show that it adequately represents the real world for the purposes of the intended use. Part of the validation process should include a statistical comparison of live data to M&S output....

In the increasingly complex and data‐limited world of military defense testing, statisticians play a valuable role in many applications. Before the DoD acquires any major new capability, that system must undergo realistic testing in its intended environment with military users. Although the typical test environment is highly variable and factors are often uncontrolled, design of experiments techniques can add objectivity, efficiency, and rigor to the process of test planning. Statistical analyses help system evaluators get the most information out of limited data sets....

The goal of operation testing (OT) is to evaluate the effectiveness and suitability of military systems for use by trained military users in operationally realistic environments. Operators perform missions and make systems function. Thus, adequate OT must assess not only system performance and technical capability across the operational space, but also the quality of human-system interactions. Software systems in particular pose a unique challenge to testers. While some software systems may inherently be deterministic in nature, once placed in their intended environment with error-prone humans and highly stochastic networks, variability in outcomes often occurs, so tests often need to account for both “bug” finding and characterizing variability....

In many situations, collecting sufficient data to evaluate system performance against operationally realistic threats is not possible due to cost and resource restrictions, safety concerns, or lack of adequate or representative threats. Modeling and simulation tools that have been verified, validated, and accredited can be used to supplement live testing in order to facilitate a more complete evaluation of performance. Two key questions that frequently arise when planning an operational test are (1) which (and how many) points within the operational space should be chosen in the simulation space and the live space for optimal ability to verify and validate the M&S, and (2) once that data is collected, what is the best way to compare the live trials to the simulated trials for the purpose of validating the M&S?...