Irina Gaynanova (Texas A&M) describes why she thinks that replicability is a prerequisite for reproducibility in science and how scientists can (personally) start improving the replicability of research. We also discuss how the concepts of replicability/reproducibility can differ according to the domain-specific context and the methods used.
Please forward to any students or colleagues who would find this of interest!
Keith O’Rourke | The Logic of Statistics
Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks
Scott Cunningham | Causal Inference (The Mixtape)
Eric Daza | Important Ideas in Causal Inference
Wenting Cheng & Weidong Zhang | Advances in Biotech/Biopharma
Ruda Zhang | Gaussian Process Subspace Regression
Ruda Zhang | Math-Science Duality
Simon Mak | Integrating Science into Stats Models
Martin Goodson | Practical Data Science & The UK’s AI Roadmap
Jack Fitzsimons | Data Security, Privacy, & Artificial Intelligence
Chris Tosh | The piranha problem in statistics
Chris Holmes | AI, Digital Health, & The Alan Turing Institute
Philosophy of Data Science | Deborah Mayo | Revolutions, Reforms, and Severe Testing in Statistical Thinking
Charlotte Deane | Bioinformatics, Deepmind’s AlphaFold 2, and Llamas
Eric Schwitzgebel | Consciousness, Zombies, & First Person Data | Philosophy of Data Science
Starting a Statistics Consultancy | Janet Wittes
Philosophy of Data Science | Jingyi Jessica Li | Advancing Statistical Genomics
Mine Çetinkaya-Rundel | Advancing Open Access Data Science Education
Jingyi Jessica Li | Statistical Hypothesis Testing vs Machine Learning Binary Classification
Gualtiero Piccinini | What Are First-Person Data? | Philosophy of Data Science
Create your
podcast in
minutes
It is Free
DNA Today: A Genetics Podcast
Museum of the Missing
Strange by Nature Podcast
Sasquatch Chronicles
Hidden Brain