Facial soft tissue thicknesses: Noise, signal, and P

Abstract

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.

Turning the tables of sex distinction in craniofacial identification: Why females possess thicker facial soft tissues than males, not vice versa

“…Consequently, this component of the study will not be further discussed. For further comment on sampling error, measurement error, and other noise in FSTTs see: Stephan (), Stephan et al (), and Stephan & Simpson (2008).…”

“…While data pooling across the sexes to boost reliability is adequately justified on the grounds of group overlap, measurement errors, sampling, and other data noise (Stephan, ; Stephan et al, ; Stephan & Simpson, ), the body‐scale normalized sex‐trends add further justification to the data pooling approach and extinguishes any lingering concern about this approach's validity (İşcan & Steyn, ). There is little risk of jeopardizing any sex differences by data pooling because sexual dimorphism in FSTT is manifested most strongly relative to body mass, not the raw FSTT measurements.…”

“…While ancestral group labels are commonly plagued by severe limitations—e.g., oversimplification of continuous range of genetic variation to named groups, misinterpretation of named groups for distinct categories, and group derivation from subjective rather than objective criteria (AAPA, ; Gould, ; Jorde & Wooding, ; Montagu, ; Sauer, ; Stewart, ; Yudell, Roberts, DeSalle, & Tishkoff, )—such ancestry labels have commonly been used in the FSTT literature (see for review: Wilkinson, ). While we describe at length elsewhere how ancestral trends in FSTTs are largely overstated due to measurement noise (Stephan, b; Stephan et al, ; Stephan & Simpson, ), that does not excuse some attempt at communicating sample composition herein. Consequently, we use the term “Australian Whites” to denote Australians of self‐reported European extraction and use the terms American Whites and Blacks in accordance with the original publishing authors of that work (Manhein et al, ).…”

“…Rather than retaining sex‐separated data that display miniscule differences and which are associated with large measurement errors, data amalgamation, or pooling has been advocated to boost sample sizes and increase reliability under The Law of Large Numbers (Stephan, ; Stephan et al, ; Stephan, Munn, & Caple, ; Stephan & Simpson, ). While The Law of Large Numbers is a basic, well‐known, and a foundational statistical principle (Moore & McCabe, ), its application to sex‐categorized FSTTs has been viewed as a major procedural shift and that has slowed uptake.…”

“…However, the data could hardly be more numerous and/or compelling—small sex differences are routinely reported by numerous authors in the literature (see for review: Stephan & Simpson, ) and with large degrees of the overlap between male and female distributions (Stephan et al, ). Together with large measurement errors (noise), this leaves little justification for retaining separate datasets and mandates sex category data pooling (Stephan et al, ). The hesitation to embrace this approach, evidenced by continued sex categorization post‐2008 (De Greef et al, ; Dong et al, ; Drgáčová et al, ; Hwang et al, ; Jia et al, ; Wang et al, ) is underpinned by an adherence to past‐practice for past‐practice's sake, succinctly summarized by İşcan and Steyn—“until otherwise proven, it is probably best to still use sex‐ and population‐specific data” (İşcan & Steyn, , p. 371).…”