Assessing background particulate contamination in an historic building – surface lead loading and contamination

References

• Agresti, A. 2001. Exact inference for categorical data: Recent advances and continuing controversies. Stat. Med. 20:2709–22. doi:10.1002/sim.738.
   PubMed Web of Science ®Google Scholar
• Beauchamp, C., D. Ceballos, and B. King. 2017. Lessons learned from surface wipe sampling for lead in three workplaces. J. Occup. Environ. Hyg. 14 (8):609–17. doi:10.1080/15459624.2017.1309047.
   Web of Science ®Google Scholar
• Biau, D. J., B. M. Jolles, and R. Porcher. 2010. P value and the theory of hypothesis testing: An explanation for new researchers. Clin. Orthop. Relat. Res. 468:885–92. doi:10.1007/s11999-0091164-4.
   PubMed Web of Science ®Google Scholar
• California Environmental Protection Agency. 2003. Memorandum from S. Powell to J. P. Frank, Worker Health and Safety Branch. Why Worker Health and Safety Branch uses arithmetic means in exposure assessments, September 22.
   Google Scholar
• Chernoff, H. 2003. A view of past present and future. Indian J. Stat. San Antonio Conf. Sel. Articles 64 (A2):183–94.
   Google Scholar
• David, H. A. 2008. The beginnings of randomization tests. Am. Stat. 62:70–72. doi:10.1198/000313008X269576.
   Web of Science ®Google Scholar
• DiNocera, F., and F. Ferlazzo. 2000. Resampling approach to statistical inference: Bootstrapping from event-related potentials data. Behav. Res. Methods Instrum. Comput. 32 (1):111–19. doi:10.3758/BF03200793.
   PubMedGoogle Scholar
• Dixon, S., J. Wilson, C. Kawecki, R. Green, J. Phoenix, W. Galke, S. Clark, and J. Breysse. 2008. Selecting a lead hazard control strategy on dust lead loading and housing condition: Methods and results. J. Occup. Environ. Hyg. 8:530–39. doi:10.1080/15459620802219799.
   Google Scholar
• Dixon, S. L., J. W. Wilson, C. S. Clark, W. A. Galke, P. A. Succop, and M. Chen. 2005. The influence of common area lead hazards and lead hazard control on dust lead loadings in multiunit buildings. J. Occup. Environ. Hyg. 2:659–66. doi:10.1080/15459620500403737.
   PubMed Web of Science ®Google Scholar
• Ernst, M. D. 2004. Permutation methods: A basis for exact inference. Stat. Sci. 19:676–85. doi:10.1214/088342304000000396.
   Web of Science ®Google Scholar
• Fisher, R. A. [1971a] 2003. The principles of experimentation, illustrated by a psycho-social experiment. Chap. II in The design of experiments. 8th ed.,11–25. New York: Haffner Publishing. Reprinted in Statistical methods experimental design and scientific inference. New York: Oxford University Press.
   Google Scholar
• Fisher, R. A. [1971b] 2003. Introduction. Chap.I in The design of experiments, 8th ed., 1–10. New York: Haffner Publishing. Reprinted in Statistical methods experimental design and scientific inference. New York: Oxford University Press.
   Google Scholar
• Fisher, R. A. [1971c] 2003. The generalisation of null hypotheses. Fiducial probability. Chap. X in The design of experiments, 8th ed., 184–213. New York: Haffner Publishing. Reprinted in Statistical methods experimental design and scientific inference. New York: Oxford University Press.
   Google Scholar
• Gliner, J. A., N. L. Leech, and G. A. Morgan. 2002. Problems with null hypothesis significance testing (NHST): What do the textbooks say? J. Exp. Educ. 71:83–92. doi:10.1080/00220970209602058.
   Web of Science ®Google Scholar
• Goodman, S. 2008. A dirty dozen: Twelve p-value misconceptions. Semin. Hematol. 45:135–40. doi:10.1053/j.seminhematol.2008.04.003.
   PubMed Web of Science ®Google Scholar
• Haggstrom, O. 2017. The need for nuance in the null hypothesis testing debate. Educ. Psychol. Meas. 77:616–30. doi:10.1177/0013164416668233.
   PubMed Web of Science ®Google Scholar
• Hubbard, R., and M. J. Bayarri. 2003. Confusion over measures of evidence (p’s) versus errors (α’s) in classical statistical testing. Am. Stat. 57:171–82. doi:10.1198/0003130031856.
   Web of Science ®Google Scholar
• Hurlbert, S. H., and C. M. Lombardi. 2009. Final collapse of the Neyman-Pearson decision theoretic framework and rise of the neoFisherian. Ann. Zool. Fenici. 46:311–49. doi:10.5737/086.048.0501.
   Web of Science ®Google Scholar
• Johnson, D., D. Thompson, R. Clinkenbeard, and J. Redus. 2008. Professional judgment and interpretation of viable mold air sampling data. J. Occup. Environ. Hyg. 5:656–63. doi:10.1080/15459620802310796.
   PubMed Web of Science ®Google Scholar
• Kass, R. E. 2011. Statistical inference: The big picture. Stat. Sci. 26:1–9. doi:10.1214/10-STS337.
   PubMed Web of Science ®Google Scholar
• Lange, J. H. 2001. A suggested lead surface concentration standard for final clearance of floors in Commercial and industrial buildings. Indoor Built Environ. 10:48–51. doi:10.1177/1420326X010100010.
   Web of Science ®Google Scholar
• Lehmann, E. L. 1993. The Fisher, Neyman-Pearson theories of testing hypotheses: One theory or two? J. Am. Stat. Assoc. 88:1242–49. doi:10.1080/01621459.1993.10476404.
   Web of Science ®Google Scholar
• Ludbrook, J. 1994. Advantages of permutation (randomization) tests in clinical and experimental pharmacology and physiology. Clin. Exp. Pharmacol. Physiol. 21:673–86. doi:10.1111/j.1440-1681.1994.tb02570.x.
   PubMed Web of Science ®Google Scholar
• Ludbrook, J., and H. Dudley. 1998. Why permutation tests are superior to t and F tests in biomedical Research. Am. Stat. 52:127–32.
   Web of Science ®Google Scholar
• Modarres, R., J. L. Gastwirth, and W. Ewens. 2005. A cautionary note on the use of non-parametric tests in the analysis of environmental data. Environmetrics 16:319–26. doi:10.1002/env.695.
   Web of Science ®Google Scholar
• Moran, J. L., and P. Solomon. 2002. Worrying about normality. Crit. Care Resuscitation 4:316–19.
   PubMedGoogle Scholar
• Sanderson, W. T., S. Leonard, D. Ott, L. Fuortes, and W. Field. 2008. Beryllium surface levels in a Military Ammunition Plant. J. Occup. Environ. Hyg. 5:475–81. doi:10.1080/15459620802131408.
   PubMed Web of Science ®Google Scholar
• Spicer, R. C., and H. J. Gangloff. 2000. A probability model for evaluating contaminant data from an environmental event. J. Air Waste Manage. Assoc. 50:1637–46. doi:10.1080/10473289.2000.10464198.
   Web of Science ®Google Scholar
• Spicer, R. C., and H. J. Gangloff. 2003. Bioaerosol data distribution: Probability and implications for sampling in evaluating problematic buildings. Appl. Ind. Hyg. 18:584–90. doi:10.1080/10473220301411.
   Google Scholar
• Spicer, R. C., and H. J. Gangloff. 2008. Verifying interpretive criteria for bioaerosol data using (bootstrap) Monte Carlo techniques. J. Occup. Environ. Hyg. 5:85–92. doi:10.1080/15459620701804717.
   PubMed Web of Science ®Google Scholar
• Spicer, R. C., and H. J. Gangloff. 2010. Differences in detection frequency as a bioaerosol data criterion for evaluating suspect fungal contamination. Build. Environ. 45:1304–11. doi:10.1016/j.buildenv.2009.11.012.
   Web of Science ®Google Scholar
• Spicer, R. C., and H. J. Gangloff. 2011. Implications of error rates associated with numerical criteria for airborne fungal data. Proceedings of Indoor Air 2011, the 12th International Conference on Indoor Air Quality and Climate, Austin, TX 1:398–404.
   Google Scholar
• Spicer, R. C., and H. J. Gangloff. 2015. The building performance model for evaluating bioaerosol data from suspect indoor environments. Indoor Built Environ. 24:640–49. doi:10.1177/1420326X14535790.
   Web of Science ®Google Scholar
• Spicer, R. C., and H. J. Gangloff. 2016. Permutation/randomization-based inference for environmental data. Environ. Monit. Assess. 188:147. doi:10.1007/s10661-016-5090-0.
   PubMed Web of Science ®Google Scholar
• Sterne, J. A. C. 2002. Teaching hypothesis tests-time for a significant change? Stat. Med. 21:985–94. doi:10.1002/sim.1129.
   PubMed Web of Science ®Google Scholar
• Stewart-Oaten, A. 1995. Rules and judgments in statistics: Three examples. Ecology 76 (6):2001–09. doi:10.2307/1940736.
   Web of Science ®Google Scholar
• Szucs, D., and J. P. A. Ioannidis. 2017. When null hypothesis significance testing is unsuitable for research: A reassessment. Front. Hum. Neurosci. 11:390. doi:10.3389/inhum.2017.00390.
   PubMed Web of Science ®Google Scholar
• United States Department of Housing and Urban Development. 2012. Chap. 5 Risk Assessment in Guidelines for the evaluation and control of lead based paint hazards in housing. 2nd ed. Washington, DC: United States Department of Housing and Urban Development.
   Google Scholar
• United States Environmental Protection Agency. 1992. Statistical methods for evaluating the attainment of cleanup standards. Volume 3: reference-based standards for soils and solid media. EPA 230-R-94-004. Washington, DC: United States Environmental Protection Agency.
   Google Scholar
• United States Environmental Protection Agency. 1995. Final report sampling house dust for lead basic concepts and literature review. EPA 747-R-95-007. Washington, DC: United States Environmental Protection Agency.
   Google Scholar
• United States Environmental Protection Agency. 2002. Guidance for comparing background and chemical concentrations in soil for CERCLA sites. EPA 540-R-01-003 OSWER 9285, 7–4. Washington, DC: United States Environmental Protection Agency.
   Google Scholar
• Yates, F. 1990. Forward to statistical methods experimental design and scientific inference, vii– xxxii. New York: Oxford University Press.
   Google Scholar