[MINI] Sample Sizes

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, ...  Show more

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely for accuracy, and how ideas from social ch ...  Show more

One of the most common causes for problems we see in manuscripts at JAMA is an inappropriately calculated study sample size. This seemingly mysterious process is explained by Lynne Stokes, PhD, professor of Statistical Science at Southern Methodist University in Dallas, Texas. 

This podcast looks at how to make estimations when calculating smaller values or working with enclosed spaces like restaurants, the importance of sensitivity analyses and a new limitation of demand-driven cases. This is a very important technique which can significantly improve a ...  Show more

One of the most common causes for problems we see in manuscripts at JAMA is an inappropriately calculated study sample size. This seemingly mysterious process is explained by Lynne Stokes, PhD, professor of Statistical Science at Southern Methodist University in Dallas, Texas. 

What is better, big or small? Today the guys decide once and for all by giving their personal opinions prompted by Bob's list of items. Learn more about your ad choices. Visit megaphone.fm/adchoices