Statistics

How many success-or-failure observations should we collect from a computer simulation? Often, researchers use space-filling design of experiments when planning modeling and simulation (M&S) studies. We are not satisfied with existing guidance on justifying the number of runs when developing these designs, either because the guidance is insufficiently justified, does not provide an unambiguous answer, or is not based on optimizing a statistical measure of merit. Analysts should use confidence interval margin of error as the statistical measure of merit for M&S studies intended to characterize overall M&S behavioral trends....

Both NASA and DOT&E increasingly rely on computer models to supplement data collection, and utilize statistical distributions to quantify the uncertainty in models, so that decision-makers are equipped with the most accurate information about system performance and model fitness. This article provides a high-level overview of uncertainty quantification (UQ) through an example assessment for the reliability of a new space-suit system. The goal is to reach a more general audience in Significance Magazine, and convey the importance and relevance of statistics to the defense and aerospace communities....

Surveys play an important role in quantifying user opinion during test and evaluation (T&E). Current best practice is to use surveys that have been tested, or “validated,” to ensure that they produce reliable and accurate results. However, unvalidated (“custom”) surveys are still widely used in T&E, raising questions about how to determine sample sizes for—and interpret data from— T&E events that rely on custom surveys. In this presentation, I characterize the statistical properties of validated and custom survey responses using data from recent T&E events, and then I demonstrate how these properties affect test design, analysis, and interpretation....

In many Department of Defense test and evaluation applications, binary response variables are unavoidable. Many have considered D-optimal design of experiments for generalized linear models. However, little consideration has been given to assessing how these new designs perform in terms of statistical power for a given hypothesis test. Monte Carlo simulations and exact power calculations suggest that D optimal designs generally yield higher power than binary D-optimal designs, despite using logistic regression in the analysis after data have been collected....

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....

It is often said that many research findings – from social sciences, medicine, economics, and other disciplines – are false. This fact is trumpeted in the media and by many statisticians. There are several reasons that false research is published, but to what extent should we be worried about them in defense testing and modeling and simulation? In this talk I will present several recommendations for actions that statisticians and data scientists can take to improve the quality of our validations and evaluations....

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:

Sequential analysis concerns statistical evaluation in situations in which the number, pattern, or composition of the data is not determined at the start of the investigation, but instead depends upon the information acquired throughout the course of the investigation. Expanding the use of sequential analysis has the potential to save resources and reduce test time (National Research Council, 1998). This paper summarizes the literature on sequential analysis and offers fundamental information for providing recommendations for its use in DoD test and evaluation....

IDA analysts strive to communicate clearly and effectively. Good data visualizations can enhance reports by making the conclusions easier to understand and more memorable. The goal of this seminar is to help you avoid settling for factory defaults and instead present your conclusions through visually appealing and understandable charts. Topics covered include choosing the right level of detail, guidelines for different types of graphical elements (titles, legends, annotations, etc.), selecting the right variable encodings (color, plot symbol, etc....

Acceptance Sampling by Variables (ASbV) is a statistical testing technique used in Personal Protective Equipment programs to determine the quality of the equipment in First Article and Lot Acceptance Tests. This article intends to remedy the lack of existing references that discuss the similarities between ASbV and certain techniques used in different sub-disciplines within statistics. Understanding ASbV from a statistical perspective allows testers to create customized test plans, beyond what is available in MIL-STD-414....

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....

The fundamental purpose of test and evaluation (T&E) in the Department of Defense (DOD) is to provide knowledge to answer critical questions that help decision makers manage the risk involved in developing, producing, operating, and sustaining systems and capabilities. At its core, T&E takes data and translates it into information for decision makers. Subject matter expertise of the platform and operational mission have always been critical components of developing defensible test and evaluation strategies....

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....

Are you sick of having to manually iterate your way through sizing your design of experiments? Come learn about JEDIS, the new IDA-developed JMP Add-In for automating design of experiments power calculations. JEDIS builds multiple test designs in JMP over user-specified ranges of sample sizes, Signal-to-Noise Ratios (SNR), and alpha (1 -confidence) levels. It then automatically calculates the statistical power to detect an effect due to each factor and any specified interactions for each design....

The complex, multifunctional nature of defense systems, along with the wide variety of system types, demands a structured but flexible analytical process for testing systems. This chapter summarizes commonly used techniques in defense system testing and specific challenges imposed by the nature of defense system testing. It highlights the core statistical methodologies that have proven useful in testing defense systems. Case studies illustrate the value of using statistical techniques in the design of tests and analysis of the resulting data....

While the human brain is powerful tool for quickly recognizing patterns in data, it will frequently make errors in interpreting random data. Luckily, these mistakes occur in systematic and predictable ways. Statistical models provide an analytical framework that helps us avoid these error-prone heuristics and draw accurate conclusions from random data. This non-technical presentation highlights some tricks of the trade learned by studying data and the way the human brain processes....

In April 2016, NASA, DOT&E, and IDA collaborated on a workshopdesigned to strengthen the community around statistical approaches to test andevaluation in defense and aerospace. The workshop brought practitioners, analysts,technical leadership, and statistical academics together for a three day exchange ofinformation with opportunities to attend world renowned short courses, share commodchallenges, and learn new skill sets from a variety of tutorials. A highlight of theworkshop was the Tuesday afternoon technical leadership panel chaired by Dr....

To mark IDA’s 60th anniversary, we are conducting a series of workshops and symposia that bring together IDA sponsors, researchers, experts inside and outside government, and other stakeholders to discuss issues of the day. These events focus on future national security challenges, reflecting on how past lessons and accomplishments help prepare us to deal with complex issues and environments we face going forward. This publication represents the proceedings of the Science of Test Workshop....

This document outlines the charter for the Committee to Institutionalize Scientific Test Design and Rigor in Test and Evaluation. The charter defines the problem, identifies potential steps in a roadmap for accomplishing the goals of the committee and lists committeemembership. Once the committee is assembled, the members will revise this document as needed. The charter will be endorsed by DOT&E and DDT&E, once finalize. Suggested Citation Freeman, Laura. Statistically Based T&E Using Design of Experiments....

Recently, the principles of Design of Experiments (DOE) have been implemented as amethod of increasing the statistical rigor of operational tests. The focus has been on ensuringcoverage of the operational envelope in terms of system effectiveness. DOE is applicable inreliability analysis as well. A reliability standard, ANSI-0009, advocates the use Design forReliability (DfR) early in the product development cycle in order to design-in reliability. Robustparameter design (RPD) first used by Taguchi and then by the response surface communityprovides insights on how DOE can be used to make a products and processes invariant tochanges in factors....

There has been recent emphasis on the increased use of statistics, including the use of statistically designed experiments, to plan and execute tests that support Department of Defense (DoD) acquisition programs. The use of statistical methods, including experimental design, has shown great benefits in industry, especially when used in an integrated fashion; for example see the literature on Six Sigma. The structured approach of experimental design allows the user to determine what data need to be collected and how it should be analyzed to achieve specific decision making objectives....