Facial soft tissue thicknesses: Noise, signal, and P.
Facial soft tissue thicknesses (FSTTs) hold an important role in craniofacial identification, forming the underlying quantitative basis of craniofacial superimposition and facial approximation methods. It is, therefore, important that patterns in FSTTs be correctly described and interpreted. In prio...
| Publicado en: | Forensic Science International Vol. 257; pp. 114 - 123 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Dec2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=111495900&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111495900 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03790738 3L0 jtl: Forensic Science International issn: 03790738 maglogo: N pubinfo: dt: Dec2015 vid: 257 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 111495900 111495900 NLM26295929 111495900 10.1016/j.forsciint.2015.07.051 NLM26295929 111495900 ppf: 114 ppct: 9 formats: tig: atl: Facial soft tissue thicknesses: Noise, signal, and P. aug: au: Stephan, Carl N. Munn, Lachlan Caple, Jodi affil: Laboratory for Human Craniofacial and Skeletal Identification (HuCS-ID Lab), School of Biomedical Sciences, The University of Queensland, Brisbane, 4072, Australia sug: subj: Statistics Face Anatomy and Histology Human Reproducibility of Results Sample Size Forensic Anthropology ab: Facial soft tissue thicknesses (FSTTs) hold an important role in craniofacial identification, forming the underlying quantitative basis of craniofacial superimposition and facial approximation methods. It is, therefore, important that patterns in FSTTs be correctly described and interpreted. In prior FSTT literature, small statistically significant differences have almost universally been overemphasized and misinterpreted to reflect sex and ancestry effects when they instead largely encode nuisance statistical noise. Here we examine FSTT data and give an overview of why P-values do not mean everything. Scientific inference, not mechanical evaluation of P, should be awarded higher priority and should form the basis of FSTT analysis. This hinges upon tempered consideration of many factors in addition to P, e.g., study design, sampling, measurement errors, repeatability, reproducibility, and effect size. While there are multiple lessons to be had, the underlying message is foundational: know enough statistics to avoid misinterpreting background noise for real biological effects. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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