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PODCAST

Data Skeptic

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

technology mathematics

All Episodes

17:32

[MINI] The Chi-Squared Test

Data Skeptic ·

2015/02/06

The Chi-Squared test is a methodology for hypothesis testing. When one has categorical data, in the form of frequency counts or observations (e.g. Vegetarian, Pescetarian, and Omnivore), split into two or more categories (e.g. Male, Female), a question may arise such as "Are women more likely than men to be vegetarian?" or put more accurately, "Is any observed difference in the frequency with which women report being vegetarian differ in a statistically significant way from the frequency men report

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29:57

Mapping Reddit Topics with Randy Olson

Data Skeptic ·

2015/01/30

My quest this week is noteworthy a.i. researcher Randy Olson who joins me to share his work creating the Reddit World Map - a visualization that illuminates clusters in the reddit community based on user behavior. Randy's blog post on created the reddit world map is well complimented by a more detailed write up titled Navigating the massive world of reddit: using backbone networks to map user interests in social media. Last but not least, an interactive version of the results (which

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12:45

[MINI] Partially Observable State Spaces

Data Skeptic ·

2015/01/23

When dealing with dynamic systems that are potentially undergoing constant change, its helpful to describe what "state" they are in. In many applications the manner in which the state changes from one to another is not completely predictable, thus, there is uncertainty over how it transitions from state to state. Further, in many applications, one cannot directly observe the true state, and thus we describe such situations as partially observable state spaces. This episode explores what this

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28:25

Easily Fooling Deep Neural Networks

Data Skeptic ·

2015/01/16

My guest this week is Anh Nguyen, a PhD student at the University of Wyoming working in the Evolving AI lab. The episode discusses the paper Deep Neural Networks are Easily Fooled [pdf] by Anh Nguyen, Jason Yosinski, and Jeff Clune. It describes a process for creating images that a trained deep neural network will mis-classify. If you have a deep neural network that has been trained to recognize certain types of objects in images, these "fooling" images can be constructed in a way which the network

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10:56

[MINI] Data Provenance

Data Skeptic ·

2015/01/09

This episode introduces a high level discussion on the topic of Data Provenance, with more MINI episodes to follow to get into specific topics. Thanks to listener Sara L who wrote in to point out the Data Skeptic Podcast has focused alot about using data to be skeptical, but not necessarily being skeptical of data. Data Provenance is the concept of knowing the full origin of your dataset. Where did it come from? Who collected it? How as it collected? Does it combine independent sources or one

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31:28

Doubtful News, Geology, Investigating Paranormal...

Data Skeptic ·

2015/01/03

I had the change to speak with well known Sharon Hill (@idoubtit) for the first episode of 2015. We discuss a number of interesting topics including the contributions Doubtful News makes to getting scientific and skeptical information ranked highly in search results, sink holes, why earthquakes are hard to predict, and data collection about paranormal groups via the internet.

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09:55

[MINI] Belief in Santa

Data Skeptic ·

2014/12/26

In this quick holiday episode, we touch on how one would approach modeling the statistical distribution over the probability of belief in Santa Claus given age.

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23:43

Economic Modeling and Prediction, Charitable Giving,...

Data Skeptic ·

2014/12/19

Economist Peter Backus joins me in this episode to discuss a few interesting topics. You may recall Linhda and I previously discussed his paper "The Girlfriend Equation" on a recent mini-episode. We start by touching base on this fun paper and get a follow up on where Peter stands years after writing w.r.t. a successful romantic union. Additionally, we delve in to some fascinating economics topics. We touch on questions of the role models, for better or for worse, played a role in the ~2008

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18:04

[MINI] The Battle of the Sexes

Data Skeptic ·

2014/12/12

Love and Data is the continued theme in this mini-episode as we discuss the game theory example of The Battle of the Sexes. In this textbook example, a couple must strategize about how to spend their Friday night. One partner prefers football games while the other partner prefers to attend the opera. Yet, each person would rather be at their non-preferred location so long as they are still with their spouse. So where should they decide to go?

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58:46

The Science of Online Data at Plenty of Fish with...

Data Skeptic ·

2014/12/05

Can algorithms help you find love? Many happy couples successfully brought together via online dating websites show us that data science can help you find love. I'm joined this week by Thomas Levi, Senior Data Scientist at Plenty of Fish, to discuss some of his work which helps people find one another as efficiently as possible. Matchmaking is a truly non-trivial problem, and one that's dynamically changing all the time as new users join and leave the "pool of fish". This episode explores the

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16:11

[MINI] The Girlfriend Equation

Data Skeptic ·

2014/11/28

Economist Peter Backus put forward "The Girlfriend Equation" while working on his PhD - a probabilistic model attempting to estimate the likelihood of him finding a girlfriend. In this mini episode we explore the soundness of his model and also share some stories about how Linhda and Kyle met.

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41:45

The Secret and the Global Consciousness Project with...

Data Skeptic ·

2014/11/21

I'm joined this week by Alex Boklin to explore the topic of magical thinking especially in the context of Rhonda Byrne's "The Secret", and the similarities it bears to The Global Consciousness Project (GCP). The GCP puts forward the hypothesis that random number generators elicit statistically significant changes as a result of major world events.

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03:05

[MINI] Monkeys on Typewriters

Data Skeptic ·

2014/11/14

What is randomness? How can we determine if some results are randomly generated or not? Why are random numbers important to us in our everyday life? These topics and more are discussed in this mini-episode on random numbers. Many readers will be vaguely familar with the idea of "X number of monkeys banging on Y number of typewriters for Z number of years" - the idea being that such a setup would produce random sequences of letters. The origin of this idea was the mathemetician Borel who was

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50:19

Mining the Social Web with Matthew Russell

Data Skeptic ·

2014/11/07

This week's episode explores the possibilities of extracting novel insights from the many great social web APIs available. Matthew Russell's Mining the Social Web is a fantastic exploration of the tools and methods, and we explore a few related topics. One helpful feature of the book is it's use of a Vagrant virtual machine. Using it, readers can easily reproduce the examples from the book, and there's a short video available that will walk you through setting up the Mining the Social Web virtual

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26:11

[MINI] Is the Internet Secure?

Data Skeptic ·

2014/10/31

This episode explores the basis of why we can trust encryption. Suprisingly, a discussion of looking up a word in the dictionary (binary search) and efficiently going wine tasting (the travelling salesman problem) help introduce computational complexity as well as the P ?= NP question, which is paramount to the trustworthiness RSA encryption. With a high level foundation of computational theory, we talk about NP problems, and why prime factorization is a difficult problem, thus making it a great

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36:57

Practicing and Communicating Data Science with Jeff...

Data Skeptic ·

2014/10/24

Jeff Stanton joins me in this episode to discuss his book An Introduction to Data Science, and some of the unique challenges and issues faced by someone doing applied data science. A challenge to any data scientist is making sure they have a good input data set and apply any necessary data munging steps before their analysis. We cover some good advise for how to approach such problems.

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17:03

[MINI] The T-Test

Data Skeptic ·

2014/10/17

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.

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48:29

Data Myths with Karl Mamer

Data Skeptic ·

2014/10/10

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.

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12:18

Contest Announcement

Data Skeptic ·

2014/10/08

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

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14:31

[MINI] Selection Bias

Data Skeptic ·

2014/10/03

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

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