Statistics with computational methods, not agonizing pain

This is a mash-up of two talks that go together wonderfully:

  • “Statistics Without the Agonizing Pain,” John Rauser, Strata + Hadoop World NYC 2014
  • “Inferential Statistics With Computational Methods,” Allen Downey, PyCon 2015

Rauser’s keynote was my favorite at the Strata conference:

Some excerpts:

When I decided to learn statistics, I read several books, which I shall politely not identify. I understood none of them. Most of them simply described statistical procedures and then applied them, without any intuitive explanation. This talk was born of that frustration, and my wish that future students of statistics will learn the deep and elegant ideas at the heart of statistics, rather than a confusing grab-bag of statistical procedures. …

I am a software engineer, who was self-taught in statistics over a period of about a decade. And I remember struggling with what seemed like the most basic questions. But it doesn’t have to be this way. …

If you can program a computer, you have superpowers when it comes to learning statistics. Because being able to program allows you to tinker with the most fundamental ideas in statistics, the way you might have tinkered with electronics, or with mechanical things, or with music, or with sports. And so I want you to go out, and to attack statistical problems with a feeling of joy, in the spirit of play, and not from a position of fear and self-doubt.

To convince you of this idea, we’re going to use statistics to figure out whether drinking beer makes you more attractive to mosquitoes.

Inspiring! I wasn’t the only one impressed:

Now how to put this into action? Rauser recommends Allen Downey:

Recently at PyCon, Downey taught a hands-on tutorial, “Statistical inference with computational methods.” I didn’t attend the conference, so I’m grateful the video, slides, and code have all been shared openly.

The video is unedited and over 3 hours long, but much of that time is silent during hands-on work and breaks. So for convenience, I’ve provided an outline, with timestamp links, so you can proceed at your own pace. Enjoy!

1. Effect Size

2. Quantifying Precision

3. Hypothesis Testing

Downey also taught a Bayesian tutorial at PyCon, which I haven’t done yet, but I’m looking forward to it. Resources like these give me hope & confidence to learn more statistics.


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