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

Wanted: A Perfect Scatterplot (with Marginals)

We saw this scatterplot with marginal densities the other day, in a blog post by Thomas Wiecki:


The graph was produced in Python, using the seaborn package. Seaborn calls it a “jointplot;” it’s called a “scatterhist” in Matlab, apparently. The seaborn version also shows the strength of the linear relationship between the x and y variables. Nice.

I like this plot a lot, but we’re mostly an R shop here at Win-Vector. So we asked: can we make this plot in ggplot2? Natively, ggplot2 can add rugs to a scatterplot, but doesn’t immediately offer marginals, as above.

However, you can use Dean Attali’s ggExtra package. Here’s an example using the same data as the seaborn jointplot above; you can download the dataset here.

frm = read.csv("tips.csv")

plot_center = ggplot(frm, aes(x=total_bill,y=tip)) + 
  geom_point() +

# default: type="density"
ggMarginal(plot_center, type="histogram")

I didn’t bother to add the internal annotation for the goodness of the linear fit, though I could.


The ggMarginal() function goes to heroic effort to line up the coordinate axes of all the graphs, and is probably the best way to do a scatterplot-plus-marginals in ggplot (you can also do it in base graphics, of course). Still, we were curious how close we could get to the seaborn version: marginal density and histograms together, along with annotations. Below is our version of the graph; we report the linear fit’s R-squared, rather than the Pearson correlation.

# our own (very beta) plot package: details later
frm = read.csv("tips.csv")

ScatterHist(frm, "total_bill", "tip",
            title="Tips vs. Total Bill")


You can see that (at the moment) we’ve resorted to padding the axis labels with underbars to force the x-coordinates of the top marginal plot and the scatterplot to align; white space gets trimmed. This is profoundly unsatisfying, and less robust than the ggMarginal version. If you’re curious, the code is here. It relies on some functions in the file sharedFunctions.R in the same repository. Our more general version will do either a linear or lowess/spline smooth, and you can also adjust the histogram and density plot parameters.

Thanks to Slawa Rokicki’s excellent ggplot2: Cheatsheet for Visualizing Distributions for our basic approach. Check out the graph at the bottom of her post — and while you’re at it, check out the rest of her blog too.

R in a 64 bit world

32 bit data structures (pointers, integer representations, single precision floating point) have been past their “best before date” for quite some time. R itself moved to a 64 bit memory model some time ago, but still has only 32 bit integers. This is going to get more and more awkward going forward. What is R doing to work around this limitation?

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We discuss this in this article, the first of a new series of articles discussing aspects of “R as it is” that we are publishing with cooperation from Revolution Analytics. Continue reading R in a 64 bit world

What is new in the vtreat library?

The Win-Vector LLC vtreat library is a library we supply (under a GPL license) for automating the simple domain independent part of variable cleaning an preparation.

The idea is you supply (in R) an example general data.frame to vtreat’s designTreatmentsC method (for single-class categorical targets) or designTreatmentsN method (for numeric targets) and vtreat returns a data structure that can be used to prepare data frames for training and scoring. A vtreat-prepared data frame is nice in the sense:

  • All result columns are numeric.
  • No odd type columns (dates, lists, matrices, and so on) are present.
  • No columns have NA, NaN, +-infinity.
  • Categorical variables are expanded into multiple indicator columns with all levels present which is a good encoding if you are using any sort of regularization in your modeling technique.
  • No rare indicators are encoded (limiting the number of indicators on the translated data.frame).
  • Categorical variables are also impact coded, so even categorical variables with very many levels (like zip-codes) can be safely used in models.
  • Novel levels (levels not seen during design/train phase) do not cause NA or errors.

The idea is vtreat automates a number of standard inspection and preparation steps that are common to all predictive analytic projects. This leaves the data scientist more time to work on important domain specific steps. vtreat also leaves as much of variable selection to the down-stream modeling software. The goal of vtreat is to reliably (and repeatably) generate a data.frame that is safe to work with.

This note explains a few things that are new in the vtreat library. Continue reading What is new in the vtreat library?

I still think you can manufacture an unfair coin

In Gelman and Nolan’s paper “You Can Load a Die, But You Can’t Bias a Coin” The American Statistician, November 2002, Vol. 56, No. 4 it is argued you can’t easily produce a coin that is biased when flipped (and caught). A number of variations that can be easily biased (such as spinning) are also discussed.

Obviously Gelman and Nolan are smart and careful people. And we are discussing a well-regarded peer-reviewed article. So we don’t expect there is a major error. What we say is the abstraction they are using doesn’t match the physical abstraction I would pick. I pick a different one and I get different results. This is what I would like to discuss. Continue reading I still think you can manufacture an unfair coin

The Applied Theorist's Point of View