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There a bunch of really good popular science books around, that deal with real science, as well as the history and reasons behind current theories, while remaining extremely enjoyable to read. For example, "Chaos" by James Gleick (chaos, fractals, nonlinearity), "A brief history of time" by Stephen Hawking (physics, origin of the universe, time, blackholes), or "The Selfish Gene" by Richard Dawkins (evolution and natural selection). Some of these books present arguments (Dawkins), and some don't (Gleick). But they all serve to make it easy for those of us without in-depth scientific educations to understand otherwise difficult concepts.

Are there any such books that focus mainly on Statistics, or machine learning?

Please include a summary of what each book covers.

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I was so bold to add ML to the title, since both statistics and ML are the two top level topics of this site and otherwise one might feel tempted to ask the same question for ML. I hope this is ok. –  steffen Nov 2 '12 at 7:26
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(+1) while reading such book about topics I know is sometimes ... irritating, it clearly helps to get an outside view and hopefully ideas to explain difficult concepts to laymans. –  steffen Nov 2 '12 at 7:28
    
@steffen: yes, I was wondering whether I should include that. Frankly I'm not interested in ML at the moment, but I assumed answers would include ML-related books, since from a pop-science perspective, ML and stats are basically the same thing. Anyway, happy to include it, as it might get a couple more books, and duplication is unnecessary :) –  naught101 Nov 2 '12 at 8:39
    
@naught101 How about making this a Community Wiki? –  Momo Nov 2 '12 at 8:45
    
@Momo: happy for that to happen. I can't do it myself. –  naught101 Nov 2 '12 at 9:28
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8 Answers 8

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I suspect The Lady Tasting Tea, by David Salsberg is exactly what you want. It's very much written in a narrative style, almost like a novel, with essentially no math (as I recall), so it would be accessible to anyone. I read it long ago and really enjoyed it. It reads very fast, and could give people a sense of what statistical analysis is about and how it can help us understand the world and solve practical problems.

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Yeah, narrative is really important. I was going to include that in the question, but some of the examples I used don't have an excellent narrative (e.g. Dawkins), and I forgot. –  naught101 Nov 3 '12 at 3:23
    
Just finished reading this, and it was exactly what I was looking for, so thanks for the recommendation. I found the writing quality fairly poor, which was quite distracting at first, but I got used to it after a while. The material covered is excellent, and it gives a great historical account of where stats has come from, and what drove the people making the discoveries, and leaves you with a glimpse of what's yet to come, and an feeling of the exciting possibilities of getting more involved in the field. Might see if I can get some of my stats-hating friends to read it :) –  naught101 May 30 '13 at 14:09
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Nate Silver's new book The Signal and the Noise: Why Most Predictions Fail – But Some Don't fits your description quite well. It is also an introduction into Bayesian thinking for laypeople. It got some attention lately and a review of the book can be found here.

Also worth checking out are Levitt & Dubner's Freakonomics books.

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I would suggest the following books, though neither is ideal, you should check out:

  1. The (Mis)Behaviour of Markets by (the great) B. Mandelbrot
  2. Struck By Lightening by Jefferey Rosenthal

with the former more focused on finance, but still statsy, and the latter is a introduction to all the interesting probability subjects: odds, the Monty Hall problem, utility functions, random walks etc.

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"The Theory That Would Not Die" by Sharon Bertsch McGrayne is a very readable book on the history of Bayesian statistics and the general idea behind it without getting too bogged down in the math.

I am also a fan of "The Cartoon Guide to Statistics" by Gonnick and Smith as a nice introduction to the general concept of statistics with some of the math, but presented in a way that does not put you to sleep (I also have the cartoon guides to genetics, physics, and chemistry and have read a couple of the others).

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More good reads:

The Flaw of Averages by Sam L. Savage

Fooled By Randomness by Nassim Taleb

Both are somewhat cautionary books about being careful towards how to interpret probability and statistics in our everyday lives. For example, in financial markets, one might misuse an everyday gaussian distribution as a risk measure with disastrous consequences, and thus we might want to use more empirical based models (such as monte carlo simulations) in practice. Taleb is very popular in financial circles, and often cautions us to be more careful about behavioral biases and over-reliance on modelling

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Ian Ayres is author of the book "Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart" which discusses several examples of data mining.

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A very good book for aiding basic statistical literacy and statistical reasoning - and for making the case for these as important - is The Tiger That Isn't by Andrew Dilnot, the former presenter of a popular radio show about applied statistics for the BBC.

I often recommend it as the statistics equivalent of the popular pop science book Bad Science by Ben Goldacre. It's good for introducing basic statistical reasoning, for showing the importance of basic statistical reasoning, and getting people concerned about the lack of basic statistical reasoning among people who really should know better (particularly politicians, journalists, etc). Very accessible, engaging, funny in places, deeply worrying in others! Particularly good as an introduction for anyone who thinks of numbers as 'not their thing'.

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The Drunkard's Walk by Leonard Mlodinow is an easy to read introduction to basic stats and probability. The content is aimed at an audience with no statistical or mathematical training, and there are no equations. I found it a little too dumbed down. There are lots of anecdotes relating various applications of bad statistics, and clear explanations of why they were wrong.

The book covers basic stats and conditional probability.

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