Hunt-and-Test Procedures: Design and Derandomization — NSF Award to Regents of the University of Michigan - Ann Arbor (MI, $175,00
This project addresses a fundamental challenge in testing statistical hypotheses: reliably detecting signals from complex data while avoiding false discoveries due to "double dipping", a practice of unintentionally using the same data to both identify and test hypotheses. Double dipping inflates the rate of false findi
| Award title | Hunt-and-Test Procedures: Design and Derandomization |
|---|---|
| Award ID | 2515385 |
| Awardee | Regents of the University of Michigan - Ann Arbor |
| City | ANN ARBOR |
| State | MI |
| Amount obligated | $175,000 |
| Principal investigator | Richard Guo |
| Program | STATISTICS |
| Start date | 09/01/2025 |
| Abstract | This project addresses a fundamental challenge in testing statistical hypotheses: reliably detecting signals from complex data while avoiding false discoveries due to "double dipping", a practice of unintentionally using the same data to both identify and test hypotheses. Double dipping inflates the rate of false findings, thereby undermining the credibility and replicability of scientific research across various fields, including health, economics, and political science. To overcome this proble |
| Source | NSF Awards |
$799/mo
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