The t-test is this week's mini-episode topic. The t-test is a statistical testing procedure used to determine if the mean of two datasets differs by a statistically significant amount. We discuss how a wine manufacturer might apply a t-test to determine if the sweetness, acidity, or some other property of two separate grape vines might differ in a statistically meaningful way.

Check out more details and examiles found in the show notes linked below.

https://dataskeptic.com/blog/episodes/2014/t-test

This week I'm joined by Karl Mamer to discuss the data behind three well known urban legends. Did a large blackout in New York and surrounding areas result in a baby boom nine months later? Do subliminal messages affect our behavior? Is placing beer alongside diapers a recipe for generating more revenue than these products in separate locations? Listen as Karl and I explore these claims.

The Data Skeptic Podcast is launching a contest- not one of chance, but one of skill. Listeners are encouraged to put their data science skills to good use, or if all else fails, guess!

The contest works as follows. Below is some data about the cumulative number of downloads the podcast has achieved on a few given dates. Your job is to predict the date and time at which the podcast will recieve download number 27,182. Why this arbitrary number? It's as good as any other arbitrary number!

Use whatever means you want to formulate a prediction. Once you have it, wait until that time and then post a review of the Data Skeptic Podcast on iTunes. You don't even have to leave a good review! The review which is posted closest to the actual time at which this download occurs will win a free copy of Matthew Russell's "Mining the Social Web" courtesy of the Data Skeptic Podcast. "Price is Right" rules are in play - the winner is the person that posts their review closest to the actual time without going over.

More information at dataskeptic.com

A discussion about conducting US presidential election polls helps frame a converation about selection bias.

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.

Our guest this week is Hamilton physics professor Kate Jones-Smith who joins us to discuss the evidence for the claim that drip paintings of Jackson Pollock contain fractal patterns. This hypothesis originates in a paper by Taylor, Micolich, and Jonas titled Fractal analysis of Pollock's drip paintings which appeared in Nature. 

Kate and co-author Harsh Mathur wrote a paper titled Revisiting Pollock's Drip Paintings which also appeared in Nature. A full text PDF can be found here, but lacks the helpful figures which can be found here, although two images are blurred behind a paywall. 

Their paper was covered in the New York Times as well as in USA Today (albeit with with a much more delightful headline: Never mind the Pollock's [sic]). 

While discussing the intersection of science and art, the conversation also touched briefly on a few other intersting topics. For example, Penrose Tiles appearing in islamic art (pre-dating Roger Penrose's investigation of the interesting properties of these tiling processes), Quasicrystal designs in art, Automated brushstroke analysis of the works of Vincent van Gogh, and attempts to authenticate a possible work of Leonardo Da Vinci of uncertain provenance. Last but not least, the conversation touches on the particularly compellingHockney-Falco Thesis which is also covered in David Hockney's book Secret Knowledge. 

For those interested in reading some of Kate's other publications, many Katherine Jones-Smith articles can be found at the given link, all of which have downloadable PDFs.