David J. Hand

Statistics has evolved into an exciting discipline which uses deep theory and powerful software to shed light on the world around us: from clinical trials in medicine, to economics, sociology, and countless other subjects vital to understanding modern life. This Very Short Introduction explores and explains how statistics works today.

Statistics: A Very Short Introduction

Gavin

Was looking for a qualitative introduction to convey something of the excitement and philosophical importance - the art of discovering anything which isn't bleedin' obvious! nor knowable apriori! This has bits of that ("Statistics is applied philosophy of science"; "it is the technology for handling uncertainty") but is still too dry to recommend as a first exposure. 

He diagnoses the worst parts of university teaching: hand calculations, canned inference, and the (exhausting, interminable) bag-of-tools approach, rather than computers and The Framework. But the latter have steep learning curves. I think the biggest thing missing is simple tailoring of datasets: let them pick something they care about to study, to learn how to study on. 

Lots of ML methods covered, without a single mention of the phrase "machine learning". This is fair enough if you consider how much of (enterprise) ML hype is just rebranded 40 year old stats.

Hand notes the origins of the field - as 
  State-istics
, i.e. as the beginning of bureaucracy and surveillance. But he doesn't feel the tension of this fact: that it helped to transform us, for good and ill, into legible people.

One dodgy idea: he claims that numbers offer a more direct apprehension of reality than words, that they're realer. But this isn't why they're better: they're better because they're more sensitive - it's at least possible for them to track any size change in the world, while words are mostly stuck to medium-sized dry goods - and because they are easier to spot errors in.

Statistics: A Very Short Introduction

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