All posts by John Mount

I was wrong about statistics

I’ll admit it: I have been wrong about statistics. However, that isn’t what this article is about. This article is less about some of the statistical mistakes I have made, as a mere working data scientist, and more of a rant about the hectoring tone of corrections from some statisticians (both when I have been right and when I have been wrong).


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Used wrong (image Justin Baeder, some rights reserved).

Continue reading I was wrong about statistics

Efficient accumulation in R

R has a number of very good packages for manipulating and aggregating data (plyr, sqldf, ScaleR, data.table, and more), but when it comes to accumulating results the beginning R user is often at sea. The R execution model is a bit exotic so many R users are very uncertain which methods of accumulating results are efficient and which are inefficient.


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Accumulating wheat (Photo: Cyron Ray Macey, some rights reserved)

In this latest “R as it is” (again in collaboration with our friends at Revolution Analytics) we will quickly become expert at efficiently accumulating results in R. Continue reading Efficient accumulation in R

Text encoding is a convoluted mess

Modern text encoding is a convoluted mess where costs can easily exceed benefits. I admit we are in a world that has moved beyond ASCII (which at best served only English, and even then without full punctuation). But modern text encoding standards (utf-x, Unicode) have metastasized to the point you spend more time working around them than benefiting from them.


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ASCII Code Chart-Quick ref card” by Namazu-tron – See above description. Licensed under Public Domain via Wikimedia Commons
Continue reading Text encoding is a convoluted mess

A dynamic programming solution to A/B test design

Our last article on A/B testing described the scope of the realistic circumstances of A/B testing in practice and gave links to different standard solutions. In this article we will be take an idealized specific situation allowing us to show a particularly beautiful solution to one very special type of A/B test.

For this article we are assigning two different advertising message to our potential customers. The first message, called “A”, we have been using a long time, and we have a very good estimate at what rate it generates sales (we are going to assume all sales are for exactly $1, so all we are trying to estimate rates or probabilities). We have a new proposed advertising message, called “B”, and we wish to know does B convert traffic to sales at a higher rate than A?

We are assuming:

  • We know exact rate of A events.
  • We know exactly how long we are going to be in this business (how many potential customers we will ever attempt to message, or the total number of events we will ever process).
  • The goal is to maximize expected revenue over the lifetime of the project.

As we wrote in our previous article: in practice you usually do not know the answers to the above questions. There is always uncertainty in the value of the A-group, you never know how long you are going to run the business (in terms of events or in terms of time, and you would also want to time-discount any far future revenue), and often you value things other than revenue (valuing knowing if B is greater than A, or even maximizing risk adjusted returns instead of gross returns). This represents severe idealization of the A/B testing problem, one that will let us solve the problem exactly using fairly simple R code. The solution comes from the theory of binomial option pricing (which is in turn related to Pascal’s triangle).


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Yang Hui (ca. 1238–1298) (Pascal’s) triangle, as depicted by the Chinese using rod numerals.

For this “statistics as it should be” (in partnership with Revolution Analytics) article let us work the problem (using R) pretending things are this simple. Continue reading A dynamic programming solution to A/B test design

What is a good Sharpe ratio?

We have previously written that we like the investment performance summary called the Sharpe ratio (though it does have some limits).

What the Sharpe ratio does is: give you a dimensionless score to compare similar investments that may vary both in riskiness and returns without needing to know the investor’s risk tolerance. It does this by separating the task of valuing an investment (which can be made independent of the investor’s risk tolerance) from the task of allocating/valuing a portfolio (which must depend on the investor’s preferences).

But what we have noticed is nobody is willing to honestly say what a good value for this number is. We will use the R analysis suite and Yahoo finance data to produce some example real Sharpe ratios here so you can get a qualitative sense of the metric. Continue reading What is a good Sharpe ratio?

A bit about Win-Vector LLC

Win-Vector LLC is a consultancy founded in 2007 that specializes in research, algorithms, data-science, and training. (The name is an attempt at a mathematical pun.)

Win-Vector LLC can complete your high value project quickly (some examples), and train your data science team to work much more effectively. Our consultants include the authors of Practical Data Science with R and also the video course Introduction to Data Science. We now offer on site custom master classes in data science and R.

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Please reach out to us at contact@win-vector.com for research, consulting, or training.

Follow us on (Twitter @WinVectorLLC), and sharpen your skills by following our technical blog (link, RSS).

Why does designing a simple A/B test seem so complicated?

Why does planning something as simple as an A/B test always end up feeling so complicated?

An A/B test is a very simple controlled experiment where one group is subject to a new treatment (often group “B”) and the other group (often group “A”) is considered a control group. The classic example is attempting to compare defect rates of two production processes (the current process, and perhaps a new machine).


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Illustration: Boris Artzybasheff
(photo James Vaughan, some rights reserved)
In our time an A/B test typically compares the conversion to sales rate of different web-traffic sources or different web-advertising creatives (like industrial defects, a low rate process). An A/B test uses a randomized “at the same time” test design to help mitigate the impact of any possible interfering or omitted variables. So you do not run “A” on Monday and then “B” on Tuesday, but instead continuously route a fraction of your customers to each treatment. Roughly a complete “test design” is: how much traffic to route to A, how much traffic to route to B, and how to chose A versus B after the results are available.

A/B testing is one of the simplest controlled experimental design problems possible (and one of the simplest examples of a Markov decision process). And that is part of the problem: it is likely the first time a person will need to truly worry about:

  • Power/Significance
  • Design of experiments
  • Defining utility
  • Priors or beliefs
  • Efficiency of inference

All of these are technical terms we will touch on in this article. However, we argue the biggest sticking point of A/B testing is: it requires a lot more communication between the business partner (sponsoring the test) and the analyst (designing and implementing the test) than a statistician or data scientist would care to admit. In this first article of a new series called “statistics as it should be” (in partnership with Revolution Analytics) we will discuss some of the essential issues in planning A/B tests. Continue reading Why does designing a simple A/B test seem so complicated?

Neural net image salad again (with code)

Alexander Mordvintsev, Christopher Olah, and Mike Tyka, recently posted a great research blog article where they tried to visualize what a image classification neural net “wants to see.” They achieve this by optimizing the input to correspond to a fixed pattern of neural net internal node activation. This generated truly beautiful and fascinating phantasmagorical images (or an “image salad” by analogy to word salad). It is sort of like a search for eigenfaces (but a lot more fun).

A number of researchers had previously done this (many cited in their references), but the authors added more good ideas:

  • Enforce a “natural image constraint” through insisting on near-pixel correlations.
  • Start the search from another real image. For example: if the net is internal activation is constrained to recognize buildings and you start the image optimization from a cloud you can get a cloud with building structures. This is a great way to force interesting pareidolia like effects.
  • They then “apply the algorithm iteratively on its own outputs and apply some zooming after each iteration.” This gives them wonderful fractal architecture with repeating motifs and beautiful interpolations.
  • Freeze the activation pattern on intermediate layers of the neural network.
  • (not claimed, but plausible given the look of the results) Use the access to the scoring gradient for final image polish (likely cleans up edges and improves resolution).


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From Michael Tyka’s Inceptionism gallery

Likely this used a lot of GPU cycles. The question is, can we play with some of the ideas on our own (and on the cheap)? The answer is yes.

I share complete instructions, and complete code for a baby (couple of evenings) version of related effects. Continue reading Neural net image salad again (with code)

Betting with their money

The recent The Atlantic article “The Man Who Broke Atlantic City” tells the story of Don Johnson who won millions of dollars in private room custom rules high stakes blackjack. The method Mr. Johnson reportedly used is, surprisingly, not card counting (as made famous by professor Edward O. Thorp in Beat the Dealer). It is instead likely an amazingly simple process I will call a martingale money pump. Naturally the Atlantic wouldn’t want to go into the math, but we can do that here.


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Blackjack Wikimedia
Continue reading Betting with their money

I do not believe Google invented the term A/B test

The June 4, 2015 Wikipedia entry on A/B Testing claims Google data scientists were the origin of the term “A/B test”:

Google data scientists ran their first A/B test at the turn of the millennium to determine the optimum number of results to display on a search engine results page.[citation needed] While this was the origin of the term, very similar methods had been used by marketers long before “A/B test” was coined. Common terms used before the internet era were “split test” and “bucket test”.

It is very unlikely Google data scientists were the first to use the informal shorthand “A/B test.” Test groups have been routinely called “A” and “B” at least as early as the 1940s. So it would be natural for any working group to informally call their test comparing abstract groups “A” and “B” an “A/B test” from time to time. Statisticians are famous for using the names of variables (merely chosen by convention) as formal names of procedures (p-values, t-tests, and many more).

Even if other terms were dominant in earlier writing, it is likely A/B test was used in speech. And writings of our time are sufficiently informal (or like speech) that they should be compared to earlier speech, not just earlier formal writing.

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That being said, a quick search yields some examples of previous use. We list but a few below. Continue reading I do not believe Google invented the term A/B test