Data Skeptic

The Credible Hulk joins me in this episode to discuss a recent blog post he wrote about glyphosate and the data about how it's introduction changed the historical usage trends of other herbicides. Links to all the sources and references can be found in the blog post.

In this discussion, we also mention the food babe and Last Thursdayism which may be worth some further reading. Kyle also mentioned the list of ingredients or chemical composition of a banana.

Credible Hulk mentioned the Mommy PhD facebook page. An interesting article about Mommy PhD can be found here. Lastly, if you enjoyed the show, please "Like" the Credible Hulk facebook group.

More features are not always better! With an increasing number of features to consider, machine learning algorithms suffer from the curse of dimensionality, as they have a wider set and often sparser coverage of examples to consider. This episode explores a real life example of this as Kyle and Linhda discuss their thoughts on purchasing a home.

The curse of dimensionality was defined by Richard Bellman, and applies in several slightly nuanced cases. This mini-episode discusses how it applies on machine learning.

This episode does not, however, discuss a slightly different version of the curse of dimensionality which appears in decision theoretic situations. Consider the game of chess. One must think ahead several moves in order to execute a successful strategy. However, thinking ahead another move requires a consideration of every possible move of every piece controlled, and every possible response one's opponent may take. The space of possible future states of the board grows exponentially with the horizon one wants to look ahead to. This is present in the notably useful Bellman equation.

This episode discusses video game analytics with guest Anders Drachen. The way in which people get access to games and the opportunity for game designers to ask interesting questions with data has changed quite a bit in the last two decades. Anders shares his insights about the past, present, and future of game analytics. We explore not only some of the innovations and interesting ways of examining user experience in the gaming industry, but also touch on some of the exciting opportunities for innovation that are right on the horizon.

You can find more from Anders online at andersdrachen.com, and follow him on twitter @andersdrachen

This mini-episode discusses Anscombe's Quartet, a series of four datasets which are clearly very different but share some similar statistical properties with one another. For example, each of the four plots has the same mean and variance on both axis, as well as the same correlation coefficient, and same linear regression.

The episode tries to add some context by imagining each of these datasets as data about a sports team, and why it can be important to look beyond basic summary statistics when exploring your dataset.

A recent episode of the Skeptics Guide to the Universe included a slight rant by Dr. Novella and the rouges about a shortcoming in operating systems. This episode explores why such a (seemingly obvious) flaw might make sense from an engineering perspective, and how data science might be the solution.

In this solo episode, Kyle proposes the concept of "annoyance mining" - the idea that with proper logging and enough feedback, data scientists could be provided the right dataset from which they can detect flaws and annoyances in software and other systems and automatically detect potential bugs, flaws, and improvements which could make those systems better.

As system complexity grows, it seems that an abstraction like this might be required in order to keep maintaining an effective development cycle. This episode is a bit of a soap box for Kyle as he explores why and how we might track an appropriate amount of data to be able to make better software and systems more suited for the users.

Elizabeth Lee from CyArk joins us in this episode to share stories of the work done capturing important historical sites digitally. CyArk is a non-profit focused on using technology to preserve the world's important historic and cultural locations digitally. CyArk's founder Ben Kacyra, a pioneer in 3D capture technology, and his wife, founded CyArk after seeing the need to preserve important artifacts and locations digitally before they are lost to natural disasters, human destruction, or the passage of time. We discuss their technology, data, and site selection including the upcoming themes of locations and the CyArk 500.

Elizabeth puts out the call to all listeners to share their opinions on what important sites should be included in The Cyark 500 Challenge - an effort to digitally preserve 500 of the most culturally important heritage sites within the next five years. You can Nominate a site by submitting a short form at CyArk.org

Visit http://www.cyark.org/projects/ to view an immersive, interactive experience of many of the sites preserved.

Linhda and Kyle review a New York Times article titled How Your Hometown Affects Your Chances of Marriage. This article explores research about what correlates with the likelihood of being married by age 26 by county. Kyle and LinhDa discuss some of the fine points of this research and the process of identifying factors for consideration.

With the advent of algorithms capable of beating highly ranked chess players, the temptation to cheat has emmerged as a potential threat to the integrity of this ancient and complex game. Yet, there are aspects of computer play that are measurably different than human play. Dr. Kenneth Regan has developed a methodology for looking at a long series of modes and measuring the likelihood that the moves may have been selected by an algorithm.

The full transcript of this episode is well annotated and has a wealth of excellent links to the things discussed.

If you're interested in learning more about Dr. Regan, his homepage (Kenneth Regan), his page on wikispaces, and the amazon page of books by Kenneth W. Regan are all great resources.

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations) that an observation is away from the mean of the population. A closely related top is the 68-95-99.7 rule which tells us that (approximately) 68% of a normally distributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3.

Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below. They further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of a finding can be said to be 3σ, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

This week Noelle Sio Saldana discusses her volunteer work at Crisis Text Line - a 24/7 service that connects anyone with crisis counselors. In the episode we discuss Noelle's career and how, as a participant in the Pivotal for Good program (a partnership with DataKind), she spent three months helping find insights in the messaging data collected by Crisis Text Line. These insights helped give visibility into a number of different aspects of Crisis Text Line's services. Listen to this episode to find out how!

If you or someone you know is in a moment of crisis, there's someone ready to talk to you by texting the shortcode 741741.