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 basis for the RSA encryption algorithm, which most of the internet uses to encrypt data.  Unlike the encryption scheme Ray Romano used in "Everybody Loves Raymond", RSA has nice theoretical foundations.

It should be noted that although this episode gives good reason to trust that properly encrypted data, based on well choosen public/private keys where the private key is not compromised, is safe.  However, having safe encryption doesn't necessarily mean that the Internet is secure.  Topics like Man in the Middle attacks as well as the Snowden revelations are a topic for another day, not for this record length "mini" episode.

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

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.

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 topic for this week is "noise" as in signal vs. noise.  This is not a signal processing discussions, but rather a brief introduction to how the work noise is used to describe how much information in a dataset is useless (as opposed to useful).

Also, Kyle announces having recently had the pleasure of appearing as a guest on The Conspiracy Skeptic Podcast to discussion The Bible Code.  Please check out this other fine program for this and it's many other great episodes.

In this week's mini episode, Linhda and Kyle discuss Ant Colony Optimization - a numerical / stochastic optimization technique which models its search after the process ants employ in using random walks to find a goal (food) and then leaving a pheremone trail in their walk back to the nest.  We even find some way of relating the city of San Francisco and running a restaurant into the discussion.

This episode loosely explores the topic of Experimental Design including hypothesis testing, the importance of statistical tests, and an everyday and business example.

In this minisode, we discuss Bayesian Updating - the process by which one can calculate the most likely hypothesis might be true given one's older / prior belief and all new evidence.