AI Article Synopsis
- Scientists ideally want to create analyses that clearly reveal the true answers to their research questions, but this is complicated by many different methods that can lead to conflicting results.
- Reproducibility is important for validating results, but it alone doesn't guarantee their broader applicability or reliability.
- This study emphasizes the importance of accepting and utilizing variability in data analysis, particularly in brain imaging, to improve result quality and enhance generalizability.
Article Abstract
In a perfect world, scientists would develop analyses that are guaranteed to reveal the ground truth of a research question. In reality, there are countless viable workflows that produce distinct, often conflicting, results. Although reproducibility places a necessary bound on the validity of results, it is not sufficient for claiming underlying validity, eventual utility, or generalizability. In this work we focus on how embracing variability in data analysis can improve the generalizability of results. We contextualize how design decisions in brain imaging can be made to capture variation, highlight examples, and discuss how variability capture may improve the quality of results.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528113 | PMC |
| http://dx.doi.org/10.1038/s41467-024-53743-y | DOI Listing |
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