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The idea of misinterpreting metrics is a very general idea and is not specific to statistics.

Humans want to distill vast amounts of information to a more manageable amount, like for example a single number.

Equity analysts look at accounting metrics, psychologists look at psychometrics test cores, doctors look at some function of blood pressure, etc etc.

Any person with deductive and sceptical mental faculties in place, will recognize that these are all simplifications, and cannot be used to deliver a unified truth.

Also, being aware of this has very little to do with being technical or not (for example, plenty of programmers only look at a CPU's clock speed to gauge performance).

Anyhow, nice post.

I share your sentiment.

I got a 13" TB in Novemember, and I just had to return it to an apple store and have the keyboard replaced because the shift/enter key was stuck. The TB (100 % gimmick) also sometimes randomly looses light on some of the buttons (which re-appear when I touch them)

I thought I would get used to the keyboard, but I dislike it so much that I am selling it and getting a 2015 MBP instead. This proved to be one gigantic time-wasting, expensive, and annoying experience.

Very cool, but does anyone know more about the plan to offer a "lib pandas" mentioned here?https://mail.python.org/pipermail/pandas-dev/2015-December/0...

That article almost gave me cancer.

I got a maxxed out 13 inch (touch bar) a couple of days ago, and I hate the new keyboard (I never got used to the shallow keyboard on the 12", after having one for almost a year).

The Touch Bar is also pretty much a useless gimmick.

Besides that it's a neat machine, though I think Apple is getting close to what is worth paying the Apple tax for (inconsistent product lineup: no USB-C on the iPhone 7, no USB-C on the external trackpads/keyboards, etc). My employer paid for my machine, I do not think I would have spent my own money on this.

No offense intended, but what is your background?

I'm a researcher with a physics/stats PhD, and if a colleague approached me and said "stochastic gradient algorithms" entails three highly complex areas of scientific knowledge, I would have been stunned and assumed an undergrad with an English major had stumbled into our lab.

Just because you find something extremely challenging, doesn't mean it is inherently challenging. Considering what a lot of people in my field is struggling with, your example is absolutely trivial. You might want to adjust your ego downwards a bit.

This problem also has an "infamous" and very simpe, elegant solution described in Programming Pearls:

"The idea is to give words that are anagrams a common signature, sort all the words together by their signatures, and then print them."

https://www.gnu.org/software/gawk/manual/html_node/Anagram-P...