Year: 2025
Type: article
Abstract: Plain Language Summary This article highlights the importance of experienced scientists sharing their knowledge acquired over decades, summarized in one focused article that could guide future generations. It reflects on a career of using principal component analysis (PCA), which helps researchers make sense of complex data in plant and agricultural studies. Through real examples, the article shows how PCA can be misunderstood or misused and offers practical tips to avoid common mistakes, as it can uncover valuable patterns in data. This should encourage scientists to focus on biological meaning when analyzing results.
Source: Agronomy Journal
DOI: 10.1002/agj2.70151
Author: Margarita Hartlieb
Institution: Technische Universität Darmstadt
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,800
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4414032479