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Incremental Sampling Methodology: Applications for Background Screening Assessments

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  • Penelope S. Pooler
  • Philip E. Goodrum
  • Deana Crumbling
  • Leah D. Stuchal
  • Stephen M. Roberts

Abstract

This article presents the findings from a numerical simulation study that was conducted to evaluate the performance of alternative statistical analysis methods for background screening assessments when data sets are generated with incremental sampling methods (ISMs). A wide range of background and site conditions are represented in order to test different ISM sampling designs. Both hypothesis tests and upper tolerance limit (UTL) screening methods were implemented following U.S. Environmental Protection Agency (USEPA) guidance for specifying error rates. The simulations show that hypothesis testing using two‐sample t‐tests can meet standard performance criteria under a wide range of conditions, even with relatively small sample sizes. Key factors that affect the performance include unequal population variances and small absolute differences in population means. UTL methods are generally not recommended due to conceptual limitations in the technique when applied to ISM data sets from single decision units and due to insufficient power given standard statistical sample sizes from ISM.

Suggested Citation

  • Penelope S. Pooler & Philip E. Goodrum & Deana Crumbling & Leah D. Stuchal & Stephen M. Roberts, 2018. "Incremental Sampling Methodology: Applications for Background Screening Assessments," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 194-209, January.
    • Handle: RePEc:wly:riskan:v:38:y:2018:i:1:p:194-209
    DOI: 10.1111/risa.12820
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    References listed on IDEAS

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    1. Young, Derek S., 2010. "tolerance: An R Package for Estimating Tolerance Intervals," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i05).
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