The following article is getting quite a lot of press right now: David Just and Brian Wansink (2015), “Fast Food, Soft Drink, and Candy Intake is Unrelated to Body Mass Index for 95% of American Adults”, Obesity Science & Practice, forthcoming (upcoming in a new pay for placement journal). Obviously it is a sensational contrary position (some coverage: here, here, and here).
I thought I would take a peek to learn about the statistical methodology (see here for some commentary). I would say the kindest thing you can say about the paper is: its problems are not statistical.
At this time the authors don’t seem to have supplied their data preparation or analysis scripts and the paper “isn’t published yet” (though they have had time for a press release), so we have to rely on their pre-print. Read on for excerpts from the work itself (with commentary). Continue reading Fast food, fast publication
It has been popular to complain that the current terms “data science” and “big data” are so vague as to be meaningless. While these terms are quite high on the hype-cycle, even the American Statistical Association was forced to admit that data science is actually a real thing and exists.
Gartner hype cycle (Wikipedia).
Given we agree data science exists, who is allowed to call themselves a data scientist? Continue reading Who is allowed to call themselves a data scientist?
There remains a bit of a two-way snobbery that Frequentist statistics is what we teach (as so-called objective statistics remain the same no matter who works with them) and Bayesian statistics is what we do (as it tends to directly estimate posterior probabilities we are actually interested in). Nina Zumel hit the nail on the head when she wrote an article explaining the appropriateness of the type of statistical theory depends on the type of question you are trying to answer, not on your personal prejudices.
We will discuss a few more examples that have been in our mind, including one I am calling “baking priors.” This final example will demonstrate some of the advantages of allowing researchers to document their priors.
Figure 1: two loaves of bread.
Continue reading Baking priors
One of the things I like about R is: because it is not used for systems programming you can expect to install your own current version of R without interference from some system version of R that is deliberately being held back at some older version (for reasons of script compatibility). R is conveniently distributed as a single package (with automated install of additional libraries).
Want to do some data analysis? Install R, load your data, and go. You don’t expect to spend hours on system administration just to get back to your task.
Python, being a popular general purpose language does not have this advantage, but thanks to Anaconda from Continuum Analytics you can skip (or at least delegate) a lot of the system environment imposed pain. With Anaconda trying out Python packages (Jupyter, scikit-learn, pandas, numpy, sympy, cvxopt, bokeh, and more) becomes safe and pleasant. Continue reading Thumbs up for Anaconda
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).
Used wrong (image Justin Baeder, some rights reserved).
Continue reading I was wrong about statistics
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.
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
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. 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.
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
In this note am going to recount “my favorite R bug.” It isn’t a bug in R. It is a bug in some code I wrote in R. I call it my favorite bug, as it is easy to commit and (thanks to R’s overly helpful nature) takes longer than it should to find.
Continue reading My favorite R bug
As an R programmer have you every wondered what can be in a
data.frame column? Continue reading What can be in an R data.frame column?
Just a warning: double check your return types in R, especially when using different modeling packages. Continue reading Check your return types when modeling in R