The application of the central limit theorem and the law of large numbers to facial soft tissue depths: T-Table robustness and trends since 2008

Stephan, Carl N. (2014) The application of the central limit theorem and the law of large numbers to facial soft tissue depths: T-Table robustness and trends since 2008. Journal of Forensic Sciences, 59 2: 454-462. doi:10.1111/1556-4029.12328


Author Stephan, Carl N.
Title The application of the central limit theorem and the law of large numbers to facial soft tissue depths: T-Table robustness and trends since 2008
Journal name Journal of Forensic Sciences   Check publisher's open access policy
ISSN 0022-1198
1556-4029
Publication date 2014-03
Year available 2013
Sub-type Article (original research)
DOI 10.1111/1556-4029.12328
Open Access Status
Volume 59
Issue 2
Start page 454
End page 462
Total pages 9
Place of publication Hoboken, NJ, United States
Publisher Wiley-Blackwell
Collection year 2014
Language eng
Formatted abstract
By pooling independent study means (x-bar), the T-Tables use the central limit theorem and law of large numbers to average out study-specific sampling bias and instrument errors and, in turn, triangulate upon human population means (μ). Since their first publication in 2008, new data from >2660 adults have been collected (c.30% of the original sample) making a review of the T-Table's robustness timely. Updated grand means show that the new data have negligible impact on the previously published statistics: maximum change = 1.7 mm at gonion; and ≤1 mm at 93% of all landmarks measured. This confirms the utility of the 2008 T-Table as a proxy to soft tissue depth population means and, together with updated sample sizes (8851 individuals at pogonion), earmarks the 2013 T-Table as the premier mean facial soft tissue depth standard for craniofacial identification casework. The utility of the T-Table, in comparison with shorths and 75-shormaxes, is also discussed.
Keyword Craniofacial identification
Face
Facial approximation
Facial reconstruction
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Biomedical Sciences Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
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