Hatching seeds before they're counted
Graphical methods for interpreting archaeobotanical data
Bibliographic Data
| ID | 5377852 |
|---|---|
| Authors | Walton A Green (Bridge University, corresponding author) |
| Year | 2009 |
| Volume | 1 |
| Issue | 1 |
| Pages | 1-13 |
| Publication date | 2009-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Archaeological and Anthropological Sciences (JOURNAL) |
| Journal identifiers | ISSN: 1866-9557 • E-ISSN: 1866-9565 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12520-009-0001-8 |
| OpenAlex | W1592105283 |
| Language | EN |
| References cited | 15 |
Like other analytic aspects of archaeology, archaeobotany has been growing progressively more quantitative in the past few decades. This may be a sign of the proliferation of increasingly mature and sophisticated methodologies for analyzing botanical data, but associated with the sophistication of quantitative methods is their inherent opacity: the value and applicability of anthropological conclusions drawn from quantitative archaeobotanical data are not only limited by the amount of information that can be extracted from data by sophisticated statistical tools, but also by our ability to draw reasonable anthropological-as opposed to merely statistical-conclusions. Even the words “classification” and “significance” have different meanings in statistics and in anthropology. In this paper, I propose the use of graphical analysis for archaeobotanical data in addition to, or instead of, typical statistical tools like significance tests, variable reduction, and clustering. Applied to data from charred seed assemblages from the ancient Near East, the visual representation of quantitative data has the advantage of handling semiquantitative data better and being interpretable without reliance on the paradigm of a formal statistical test
Archaeology · Cluster analysis · Geography · Representation (politics · Social science · Sociology · Sophistication · Statistical hypothesis testing · Statistics · Botany, Ecology, and Taxonomy Studies · Computer Science · Mathematics · Plant and animal studies · Sensory Analysis and Statistical Methods · Artificial Intelligence
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| Highly cited | No |
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