Commute times and BBQ invites help frame a discussion about the statistical concept of confidence intervals.

A discussion about getting ready in the morning, negotiating a used car purchase, and selecting the best AirBnB place to stay at help frame a conversation about the decision theoretic principal known as the Value of Information equation.

In this bonus episode, guest Louis Zocchi discusses his background in the gaming industry, specifically, how he became a manufacturer of dice designed to produce statistically uniform outcomes. 

During the show Louis mentioned a two part video listeners might enjoy: part 1 and part 2 can both be found on youtube. 

Kyle mentioned a robot capable of unnoticably cheating at Rock Paper Scissors / Ro Sham Bo. More details can be found here. 

Louis mentioned dice collector Kevin Cook whose website is DiceCollector.com 

While we're on the subject of table top role playing games, Kyle recommends these two related podcasts listeners might enjoy: 

The Conspiracy Skeptic podcast (on which host Kyle was recently a guest) had a great episode "Dungeons and Dragons - The Devil's Game?" which explores claims of D&Ds alleged ties to skepticism. 

Also, Kyle swears there's a great Monster Talk episode discussing claims of a satanic connection to Dungeons and Dragons, but despite mild efforts to locate it, he came up empty. Regardless, listeners of the Data Skeptic Podcast are encouraged to explore the back catalog to try and find the aforementioned episode of this great podcast. 

Last but not least, as mentioned in the outro, awesomedice.com did some great independent empirical testing that confirms Game Science dice are much closer to the desired uniform distribution over possible outcomes when compared to one leading manufacturer.

Marick Sinay from ZestFianance is our guest this weel.  This episode explores how data science techniques are applied in the financial world, specifically in assessing credit worthiness.

Linhda and Kyle talk about Decision Tree Learning in this miniepisode.  Decision Tree Learning is the algorithmic process of trying to generate an optimal decision tree to properly classify or forecast some future unlabeled element based by following each step in the tree.