Referenced Papers (8)
Veridical data science
Bin Yu
Proc. Natl. Acad. Sci. U. S. A., 2019
"The paper introduces the concept of 'Veridical Data Science' as a philosophical and conceptual framework for practicing data science responsibly with a documentation requirement."
Drug development: Raise standards for preclinical cancer research
C Glenn Begley, Lee M Ellis
Nature, 2012
"This article reported alarmingly low replication rates (11-20%) of landmark findings in preclinical oncological research, contributing to the 'replication crisis'."
Believe it or not: how much can we rely on published data on potential drug targets?
F Prinz, T Schlange, K Asadullah
Nat. Rev. Drug Discov., 2011
"This article raised concerns about reliance on published data for drug targets, contributing to the 'replication crisis'."
Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology
T Parker, Hannah Fraser, S Nakagawa, E Gould, S Griffith, P Vesk
BMC Biol., 2020
"This Nature article described how 246 biologists working on the same data sets got widely different analytical results due to varying analytical decisions."
A review of veridical data science by bin Yu and Rebecca L. barter
Yuval Benjamini, Yoav Benjamini
Issue 6.2, Spring 2024, 2024
"The speaker's new book on Veridical Data Science, co-authored with Rebecca Barter, aims to provide a philosophical and conceptual framework for practicing responsible data science."
The role of hypothesis testing in clinical trials
S.J. Cutler, S.W. Greenhouse, J. Cornfield, M.A. Schneiderman
J Chron Disease.
"Cited in a quote by S. Goodman, highlighting that scientific inference from data isn't always a formal exercise and statistics is often taught in a way that prioritizes formulas over real-world problem-solving."
Why is getting rid of P-values so hard? Musings on science and statistics
S Goodman
Am. Stat., 2019
"Cited to support the idea that statistics education should move towards a more scientific, pluralistic approach rather than just focusing on mathematical formulas."
Development and validation of MyProstateScore 2.0 to detect clinically significant prostate cancer
Jeffrey J Tosoian, Yuping Zhang, Lanbo Xiao, Cassie Xie, Nathan L Samora, Yashar S Niknafs
medRxiv, 2023
"The speaker's current research group is working to stress-test the results of this paper on prostate cancer detection, which has a higher AUC than the current standard."