Equation 1 · AI for Science and Medicine: A First-Principles Introduction
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John Ioannidis’s widely cited 2005 analysis formalized why this happens using positive predictive value: the probability that a statistically significant finding reflects a real effect rather than chance. Writing R for the pre-study odds that a tested relationship is genuinely true (the ratio of true to false relationships among everything a field is currently testing), for the false-negative rate, and for the false-positive threshold, the positive predictive value of a significant finding is
Sources cited in the article section
- [11] Why Most Published Research Findings Are False ↗
- [12] Raise Standards for Preclinical Cancer Research ↗
- [13] Estimating the Reproducibility of Psychological Science ↗
- [5] Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery ↗
- [3] Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round XIV ↗
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