Collaborative Research: Calibrated Hypothesis Testing — NSF Award to Regents of the University of Michigan - Ann Arbor (MI, $146,7
Scientific findings should come with error rates that mean what they say: among findings assigned a 5 percent chance of error, about 5 in 100 should turn out to be wrong. This standard, called calibration, underlies trusted probability claims from weather forecasting to machine learning, but it is not yet a routine par
| Award title | Collaborative Research: Calibrated Hypothesis Testing |
|---|---|
| Award ID | 2610643 |
| Awardee | Regents of the University of Michigan - Ann Arbor |
| City | ANN ARBOR |
| State | MI |
| Amount obligated | $146,715 |
| Principal investigator | Jake Soloff |
| Program | STATISTICS |
| Start date | 06/01/2026 |
| Abstract | Scientific findings should come with error rates that mean what they say: among findings assigned a 5 percent chance of error, about 5 in 100 should turn out to be wrong. This standard, called calibration, underlies trusted probability claims from weather forecasting to machine learning, but it is not yet a routine part of the statistical tools used in many large-scale scientific studies. The issue arises whenever researchers must triage long lists of possible discoveries, anomalies, or publishe |
| Source | NSF Awards |
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