On this episode of The Artists Of Data Science, we speak to the legendary Professor of Statistics, Dr. Andrew Gelman! He’s also well known for writing posts sh

On this episode of The Artists Of Data Science, we speak to the legendary Professor of Statistics, Dr. Andrew Gelman! He’s also well known for writing posts sharing his thoughts on best statistical practices in the sciences, with a frequent emphasis on what he sees as the absurd and unscientific. Link in the comments! QUOTES [00:04:16] "We've already passed peak statistics..." [00:31:00] "The alternative to good philosophy is not no philosophy, it's bad philosophy. " SHOW NOTES [00:09:07] The thee parts of Bayesian inference [00:12:05] What's the main difference between the frequentist and the Bayesian? [00:13:02] What is a workflow? [00:16:21] Iteratively building models [00:17:50] How does the Bayesian workflow differ from the frequentist workflow? [00:18:32] Why is it that what makes this statistical method effective is not what it does with the data, but what data it uses? [00:20:48] Why do Bayesians then tend to be a little bit more skeptical in their thought processes? [00:24:38] What is the usual story when it comes to statistics? And why don't you like it? [00:30:16] Why should statisticians and data scientist care about philosophy? [00:38:22] Why the least important part of data science is statistics.