Why Laws Break When Context Changes
Published by When Notes Fly
https://whennotesfly.com/concepts/principles-laws/why-laws-break-context-changes
When Notes Fly article, fact-checked and corrected.
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Every accepted correction to this page is recorded with the exact change, so readers can see how the page improved over time.
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Corrected John Ioannidis's institutional affiliation in his real 2005 JAMA study: the article claimed Stanford University, but Ioannidis was at the University of Ioannina and Tufts-New England Medical Center at the time (his Stanford appointment came in 2010). Also corrected the article's unsupported causal explanation for why highly cited findings were later contradicted or weakened - the article attributed it to patient-population selectivity, but the paper's own stated explanation is smaller sample sizes and nonrandomized study designs.
BeforeArticle cited the wrong institutional affiliation for a real researcher at the time of a specific publication, and added a causal explanation not supported by the paper itself.
AfterCorrected the affiliation to Ioannidis's real 2005-era institutions and replaced the unsupported causal explanation with the paper's actual stated reasoning.
Why: The James March 1991 Organization Science citation and the Gordon Guyatt/McMaster evidence-based-medicine citation in the same article both verified as fully accurate; the combined 32 percent figure is a correct (if unstated-as-such) sum of the paper's real 16 percent contradicted plus 16 percent weakened findings.
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Correction 29 July 2026
Emir Baycan: Corrected John Ioannidis's institutional affiliation in his real 2005 JAMA study: the article claimed Stanford University, but Ioannidis was at the University of Ioannina and Tufts-New England Medical Center at the time (his Stanford appointment came in 2010). Also corrected the article's unsupported causal explanation for why highly cited findings were later contradicted or weakened - the article attributed it to patient-population selectivity, but the paper's own stated explanation is smaller sample sizes and nonrandomized study designs.
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