Review of "Statistics" by Freedman, Pisani, and Purves (2017)

I found that I learned a lot about RCTs by going beyond RCTs and reading about causal inference. You learn why each assumption is important when it's broken.

'Causal Inference: What If' is a nice intro and freely available: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-...

I've been working on a introductory STATS book for the past couple of years and I totally understand where the OP is coming from. There are so many books out there that focus on technique (the HOW), but don't explain the reasoning (the WHY).

I guess it wouldn't be a problem if the techniques being taught in STATS101 were actually usable in the real world. A bit like driving a car: you don't need to know how internal combustion engines work, you just need to press the pedals (and not endanger others on the road). The problem is z-tests, t-tests, ANOVA, have very limited use cases. Most real-world data analysis will require more advanced models, so the STATS education is doubly-problematic: does not teach you useful skills OR teach you general principles.

I spent a lot of time researching and thinking about STATS curriculum and choosing which topics are actually worth covering. I wrote a blog post about this[1]. In the end I settled on a computation-heavy approach, which allows me to do lots of hands simulations and demonstrations of concepts, something that will be helpful for tech-literate readers, but I think also for the non-tech people, since it will be easier to learn Python+STATS than to try to learn STATS alone. Here is a detailed argument about how Python is useful for learning statistics[2].

If you're interested in seeing the book outline, you can check this google doc[3]. Comments welcome. I'm currently writing the last chapter, so hopefully will be done with it by January. I have a mailing list[4] for ppl who want to be notified when the book is ready.

[1] https://minireference.com/blog/fixing-the-statistics-curricu...

[2] https://minireference.com/blog/python-for-stats/

[3] https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUn...

[4] https://confirmsubscription.com/h/t/A17516BF2FCB41B2

Like calculus for the engineer, statistics is primarily for the social scientist. It is an applied mathenatics, or a form of physics (math in the realm world).

Math fans tend to discount and dismiss applied statistics as being not math, in a way that they don't do for physics, for some reason I don't fully grasp.

I think it's because statistics gets a bad reputation from the legions of terrible social scientists in the wild, who can easily publish false but socially interesting results that get applied to our real lives. Mathematically fraudulent physics, on the other hand, usually immediately dies in the engineering phase, leaving just a few rambling cranks that most of everyone ignores.

Also (and related) perhaps, just as dry mathematical statistics ignores real world empirical experimentation, "wet" applied statistics goes to far into ignoring the math completely, because too few empirical scientists are able to understand the math when they would wncounter itm

I've formally studied stats up to calculus-based probability and I'm now brushing up on math ahead of starting Georgia Tech's OMSCS. I feel more fluent than I've ever been but the following quoted passage from the book really hits home:

"Why does the book include so many exercises that cannot be solved by plugging into a formula? The reason is that few real-life statistical problems can be solved that way. Blindly plugging into statistical formulas has caused a lot of confusion. So this book takes a different approach: thinking."

This applies to both math and stats. I appreciate the value in grinding pure, fundamental technique but as I'm reviewing I'm missing more real-life applications. Theory feels like a plan until real-life throws you the first punch.

I'll be buying this book, thanks for the recommendation!

Oh this is a really good book. I've had it in my want-to-read pile for years. I will read the review now.