Characterization using normal or log-normal data with multiple censoring points

Douglas M. Hawkins, Gary W Oehlert

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Probability plotting methods based on the Kaplan-Meier estimator of the cumulative distribution function provide an effective way of checking the normality or log-normality of samples with multiple left censoring points. The plots also provide graphical estimates of the mean and standard deviation of the underlying data. The paper shows that the distributional and performance properties of the methods depend largely on the number of complete readings rather than on the original sample size or the percentage of censoring that occurred. On the view that parametric methods are generally preferable to non-parametric where both can be applied, this argues for choosing analysis methods on the basis of the absolute number of uncensored readings rather than their proportion in the original sample. Copyright (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish (US)
Pages (from-to)167-181
Number of pages15
Issue number2
StatePublished - 2000


  • Compliance monitoring
  • Detection limits
  • Probability plotting


Dive into the research topics of 'Characterization using normal or log-normal data with multiple censoring points'. Together they form a unique fingerprint.

Cite this