arXiv:2606.15393v1 Announce Type: cross Abstract: Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) is resource-intensive, leaving finite-data uncertainty, and the procedure should account for the inherent structure in the hypothesis space, when such structure exists. Here, we present a framework for controlling the false discovery rate both when each hypothesis is evidenced only by a finite count of null draws, leav
This foundational research addresses practical challenges in large-scale hypothesis testing, a critical component for scientific discovery, particularly in fields relying on AI and complex data analysis.
Improved False Discovery Rate control with finite resources directly enhances the reliability and efficiency of scientific and AI research, accelerating valid conclusions and reducing wasted effort.
This framework offers a more robust method for validating findings under real-world data constraints, likely leading to more trustworthy results in data-driven scientific fields.
- · AI/ML researchers
- · Drug discovery
- · Scientific research institutions
- · Data scientists
- · Inefficient research methodologies
- · Studies with poorly controlled false discovery rates
More accurate identification of true discoveries in large datasets becomes possible, especially with limited experimental data.
This could accelerate progress in fields like AI development and scientific research by making discovery processes more reliable despite resource constraints.
Improved fundamental statistical control could subtly influence the pace and direction of technological innovation dependent on empirical validation.
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Read at arXiv cs.LG