Premium
Incremental Sampling Methodology: Applications for Background Screening Assessments
Author(s) -
Pooler Penelope S.,
Goodrum Philip E.,
Crumbling Deana,
Stuchal Leah D.,
Roberts Stephen M.
Publication year - 2018
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.12820
Subject(s) - sample size determination , statistical power , sampling (signal processing) , statistics , population , computer science , statistical hypothesis testing , range (aeronautics) , sample (material) , type i and type ii errors , data mining , reliability engineering , mathematics , engineering , medicine , environmental health , chemistry , filter (signal processing) , chromatography , computer vision , aerospace engineering
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.