More Proof That "Context" is Important for Analyzing Data
Having started my career as a number cruncher back when the Earth was still cooling, I’m well aware of how important context is for the appropriate interpretation of data analysis results.
Opportunities for misinterpretation and errors related to data analysis can arise in many ways. These range from mistakes in data collection to errors in analysis, as well as failure to provide sufficient context as a framework for interpreting the results.
A recent book review in the January 16, 2025 Science, The ancestral genome’s tale, serves as a warning about placing the wrong emphasis on ancient DNA (aDNA) data.
The book under review, The Trouble with Ancient DNA by Anna Källén (University of Chicago Press, 2025. 160 pp.) argues that the challenge isn’t in the recovery and analysis of ancient DNA itself but in the "storytelling" that sometimes accompanies the analysis. This storytelling can extend beyond reconstructing what ancient humans might have looked like to broader claims about their social behavior or even discussions relevant to marginalized communities today.
It’s always tempting to extract as much insight as possible from limited data. However, as the book’s review points out, aDNA analysis often focuses on “…a few billion markers out of several billion,” while many other unknown factors—such as community dynamics and environmental influences—also shape behavior, just as they do today.
Copyright (c) 2025 by Dennis D. McDonald. The image at the top of this page was created by ChatGPT in response to the following prompt: “please create a colorful abstract image that illustrates the uncertainty about aDNA data analysis that is expressed in the following article”