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If you could go back in time and tell yourself to read a specific book at the beginning of your career as a statistician, which book would it be?

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Probably a community wiki question? – Russell S. Pierce Jul 21 '10 at 0:48
There are really three separate questions here! 1) What is the single most influential book in statistics; 2) What book should every statistician read; 3) What book have you read that you most wish you'd read much earlier. (2) and (3) probably have considerable overlap; (1) may be quite distinct. – onestop Feb 20 '11 at 8:50
This question is another way of looking at this question. I hope that it will provide a good complement, once it gets some good answers. – naught101 Feb 29 '12 at 13:50

19 Answers

up vote 25 down vote

Here are two to put on the list:

Tufte. The visual display of quantitative information
Tukey. Exploratory data analysis

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Both are worth a periodic re-reading, maybe once a decade, just to refresh the ideas. Concerning Tukey: it's great to sit down just with pencil and paper once in a while and do a deep analysis of an interesting dataset. – whuber♦ Sep 9 '10 at 22:55
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For graphics for a statistician, I prefer William Cleveland's books to Tufte's. – Peter Flom Oct 2 '12 at 22:46
up vote 23 down vote

The book from Hastie, Tibshirani and Friedman http://www-stat.stanford.edu/~tibs/ElemStatLearn/ should be in any statistician's library !

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I disagree - that one is closely related to machine learning, not statistics per se! – aL3xa Sep 20 '11 at 18:06
@aL3xa I guess this depends on what you call machine learning. However, Tibshirani www-stat.stanford.edu/~tibs is professor Health Research and Policy, and SATISTICS :) it is written on his web page (not in your mind maybe) – robin girard Oct 20 '11 at 19:14
up vote 17 down vote

I am no statistician, and I haven't read that much on the topic, but perhaps

Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century

should be mentioned? It is no textbook, but still worth reading.

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I second this. Also, there is quite a lot of suggestions for further reading which I think are useful in the book. – Chris Beeley Mar 17 '11 at 8:55
up vote 11 down vote

Darrell Huff -- How to Lie with Statistics

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Back when this was a \$3.95 and then a \$4.95 paperback, I bought copies by the dozen and gave them away to friends, clients, and anyone else who might be interested. – whuber♦ Sep 9 '10 at 22:54
up vote 9 down vote

Not a book, but I recently discovered an article by Jacob Cohen in American Psychologist entitled "Things I have learned (so far)." It's available as a pdf here.

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Thats a wonderful article, written in Cohen's lucid and conversational style. – richiemorrisroe Feb 20 '11 at 15:30
up vote 7 down vote

William Cleveland's book "The Elements of Graphing Data" or his book "Visualizing Data"

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I'm currently reading through The Elements (Visualizing Data is not in my current schools library). What is the difference between Elements & Visualizing Data? I haven't been able to find detailed enough descriptions to formulate what is exactly the difference between the two. – Andy W Nov 10 '11 at 19:36
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I agree. I think that, for statisticians, Cleveland is better than Tufte. – Peter Flom Jan 22 '12 at 12:41
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+1 to Robert Alberts, & +1 to Peter Flom (Cleveland's books are definitely better for statisticians, although Tufte's are beautiful as well, and I have read all of them). @AndyW, Elements is introductory, e.g., it has guidelines for making an informative graphic. Visualizing demonstrates how to center your data exploration process around graphics; it starts with preliminary visualization of the data, talks about the issues at hand and walks all the way through to assessing the final model (e.g., residual analysis) via graphics. The latter is much more informative than the former. – gung Jan 23 '12 at 4:03
@AndyW One of them is a bit more technical than the other (I forget which is which though!) – Peter Flom Oct 2 '12 at 22:48
up vote 6 down vote

I think every statistician should read Stigler's The History of Statistics: The Measurement of Uncertainty before 1900

It is beautifully written, thorough and it isn't a historian's perspective but a mathematician's, hence it doesn't avoid the technical details.

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up vote 5 down vote

In addition to "The History of Statistics" suggested by Graham, another Stigler book worth reading is

Statistics on the Table: The History of Statistical Concepts and Methods

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up vote 5 down vote

Long ago, Jack Kiefer's little monograph "Introduction to Statistical Inference" peeled away the mystery of a great deal of classical statistics and helped me get started with the rest of the literature. I still refer to it and warmly recommend it to strong students in second-year stats courses.

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Great recommendation, thank you -- I got a copy recently based on this and it really is quite good. – ars Oct 1 '10 at 19:14
I'm glad to hear someone else appreciates this book! – whuber♦ Oct 1 '10 at 20:14
up vote 5 down vote

Andrew Gelman's interesting book recommendations are here:

http://thebrowser.com/interviews/andrew-gelman-on-statistics

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up vote 4 down vote

On the math/foundations side: Harald Cramér's Mathematical Methods of Statistics.

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By the way, this is the earliest place I have found mention of Cramer's phi. Amazing how a lovely little sidenote in that book became a well known method many decades later. – Tal Galili Jan 5 at 22:56
up vote 3 down vote

Fooled By Randomness by Taleb

Taleb is a professor at Columbia and an options trader. He made about $800 million dollars in 2008 betting against the market. He also wrote Black Swan. He discusses the absurdity of using the normal distribution to model markets, and philosophizes on our ability to use induction.

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A terrible book, written by someone who does not understand statistics... – Xi'an Jan 22 '12 at 9:14
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Xi'an, care to expand, or provide links to some critiques? – naught101 Feb 29 '12 at 13:56
There are a bunch of comments on The Black Swan (and Taleb more generally) here – Peter Flom Oct 2 '12 at 22:50
up vote 3 down vote

For a clear exposition of what should be in social science journal articles (assistance if you're writing or peer reviewing) I like The Reviewer's Guide to Quantitative Methods in the Social Sciences. In particular I like the table desideratra as a synopsis of the minimum that a paper (article, thesis, dissertation) should contain. The chapters are separated by analysis technique, which is nice. I think the book has wider applications than "just" the social sciences as the techniques covered are used across many fields.

Quite early on, so perhaps not covered by the question, I was introduced to Ott's Introduction to Statistical Methods and Data Analysis. It's quite expensive, but is a wonderful resource at showing the underlying statistical models for various GLM methods. I dream of the day that journals require articles to contain show the formula of the statistical model tested.

For checking test assumptions, looking at the effects of various options within a test, and so forth, this is the one book I wish I had when I was studying. I have the previous edition and it is one of the best general resources I have purchased because of the clear and consistent manner in which information about the tests is laid out. It contains nice examples illustrating the test(s), and does not require the reader to have a particular statistical package in order to follow the expositions.

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up vote 3 down vote

I wouldn't argue that either of these should be considered "the most influential book... [for] statistician[s]", but for those who are just starting to learn about the topic, two helpful books are:

  1. Robert Abelson, Statistics as Principled Argument
  2. Paul Murrell, Introduction to Data Technologies
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up vote 1 down vote
  1. Michael Oakes' Statistical Inference: A Commentary for the Social and Behavioral Sciences.
  2. Elazar Pedhazur's Multiple Regression in Behavioral Research. If you can stand the immense detail and the self-important tone.

In case you're interested, I've reviewed both on Amazon and at http://www.integrativestatistics.com/favorites.htm

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up vote 1 down vote

Lots of good books already suggested. But here is another: Gerd Gigerenzer's "Reckoning With Risk" because understanding how statistics affect decisions is more important than getting all the theory right. In fact number one sin of statisticians is failing to communicate clearly. His book talks about the consequences of poor communication and how to avoid it.

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"understanding how statistics affect decisions is more important than getting all the theory right..." Ain't it the truth? I come from an architecture background, and I can tell you, sometimes theory just gets in the way... – naught101 Feb 29 '12 at 13:54

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