On-Demand Webinars

ACCOUNTING FOR NONDETECTS AND DIFFICULT RESULTS IN ENVIRONMENTAL DATA: PART 2:
Managing Non-Detects and Difficult Data in Summary Statistics, Plots and Trend Data

Non-detects are sometimes called the 'bug-a-boo' of environmental data analysis. Ask yourself:

How does one properly handle and account for these uncertain measurements, known to statisticians as 'left-censored' observations? Should they be combined with detected values? Is 'simple substitution' a valid statistical treatment? How do non-detects impact common statistical estimates and tests, such as prediction limits, confidence intervals, or trend tests? What are the best strategies for dealing with frequent non-detects? How can statistical treatment of non-detects affect site or regulatory decisions? (and we all know they do!)

This two-part webinar will tackle these questions and more, as well as provide practical, relevant examples of proper non-detect handling.

In Part 1 of this webinar, we will discuss the basic types of non-detects, common statistical models used to treat them, evaluate common non-detect strategies and guidance including simple substitution, and look at how key statistical methods have been applied to non-detects in environmental data. In Part 2, we will delve into strategies for properly adjusting for non-detects in summary statistics and plots, in confidence, prediction, and tolerance intervals, and in trend tests such as the Mann-Kendall and linear regression.

Instructors Bio

Kirk M. Cameron, Ph.D. is the founder and president of MacStat Consulting, Ltd. in Colorado Springs, a statistical consulting firm specializing in environmental statistical applications and problem solving. Began serving as a consultant to USEPA beginning in 1990, when he worked for Science Applications International Corporation (SAIC) in Northern Virginia. Currently consulting to EPA, DoD, DoE, the US Air Force, and private environmental and medical firms.

Education:
Ph.D. in Statistics, Stanford University, 1990
M.S. in Statistics, Stanford University, 1989
B.S. in Mathematics, University of Nebraska-Lincoln, 1984

Professional Recognitions:

• Primary author of EPA's 2009 Unified Guidance for the statistical analysis of groundwater monitoring data.
• Design and teach training courses on the statistical analysis of groundwater monitoring data, presented to regulators, consultants, and environmental professionals around the country. Filmed a commercial videotape training series on groundwater data analysis.
• Wrote a guidance document for EPA on the geostatistical analysis of soils data. Emphasized techniques for balancing sampling costs against uncertainty in geostatistical studies.
• Developed a publicly-available geostatistical and statistical-based software application (GTS) for the US Air Force to optimize sampling frequencies and locations at sites undergoing long-term ground-water monitoring (LTM).
• Provide expert review and analysis of groundwater monitoring programs at RCRA hazardous waste sites across the country.