Reproducibility and the Philosophy of Data with Clare Gollnick - TWiML Talk #121
Published:Mar 22, 2018 16:42
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode featuring Clare Gollnick, CTO of Terbium Labs, discussing the reproducibility crisis in science and its relevance to data science. The episode touches upon the high failure rate of experiment replication, as highlighted by a 2016 Nature survey. Gollnick shares her insights on the philosophy of data, explores use cases, and compares Bayesian and Frequentist techniques. The article promises an engaging conversation, suggesting a focus on practical applications and thought-provoking discussions within the field of data science and AI. The episode seems to offer a blend of technical discussion and philosophical considerations.
Key Takeaways
- •The podcast episode addresses the reproducibility crisis in scientific research.
- •Clare Gollnick discusses the philosophy of data and its implications for data science.
- •The episode covers Bayesian vs. Frequentist techniques and explores practical use cases.
Reference
“More than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments.”