“What are the best books about Statistics?” We looked at 145 of the top Statistics books, aggregating and ranking them so we could answer that very question!
The top 21 books, all appearing on 2 or more “Best Statistics” book lists, are ranked below with images, descriptions, and links. The remaining 100+ titles, as well as the book lists we used, are in alphabetical order at the bottom of the page.
Happy Scrolling!
Lists It Appears On:
“Taken literally, the title “All of Statistics” is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines.
The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. “
Lists It Appears On:
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Lists It Appears On:
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors’ own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation.
Lists It Appears On:
The R version of Andy Field’s hugely popular Discovering Statistics Using SPSS takes students on a journey of statistical discovery using the freeware R. Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.
Lists It Appears On:
Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to full rather than to inform.
Lists It Appears On:
“Moneyball is a quest for something as elusive as the Holy Grail, something that money apparently can’t buy: the secret of success in baseball. The logical places to look would be the front offices of major league teams, and the dugouts, perhaps even in the minds of the players themselves. Lewis mines all these possibilities—his intimate and original portraits of big league ballplayers are alone worth the price of admission—but the real jackpot is a cache of numbers—numbers!—collected over the years by a strange brotherhood of amateur baseball enthusiasts: software engineers, statisticians, Wall Street analysts, lawyers and physics professors.
What these geek numbers show—no, prove—is that the traditional yardsticks of success for players and teams are fatally flawed. Even the box score misleads us by ignoring the crucial importance of the humble base-on-balls. This information has been around for years, and nobody inside Major League Baseball paid it any mind. And then came Billy Beane, General Manager of the Oakland Athletics.”
Lists It Appears On:
“There are many textbooks which describe current methods of statistical analysis, while neglecting related theory. There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer’s Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study.
Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)
Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc.”
Lists It Appears On:
Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.
Lists It Appears On:
This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.
Lists It Appears On:
Drawing upon over 40 years of experience, the authors of Statistics, 10th Edition provide business professionals with a clear and methodical approach to essential statistical procedures. The text clearly explains the basic concepts and procedures of descriptive and inferential statistical analysis. It features an emphasis on expressions involving sums of squares and degrees of freedom as well as a strong stress on the importance of variability. This accessible approach will help business professionals tackle such perennially mystifying topics as the standard deviation, variance interpretation of the correlation coefficient, hypothesis tests, degrees of freedom, p-values, and estimates of effect size.
Lists It Appears On:
“Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You’d be surprised how many scientists are doing it wrong.
Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You’ll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics.”
Lists It Appears On:
This is the hardcover format of Statistics For Dummies, 2nd Edition. The fun and easy way to get down to business with statistics Stymied by statistics? No fear? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life. Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more. Tracks to a typical first semester statistics course Updated examples resonate with today’s students Explanations mirror teaching methods and classroom protocol Packed with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance.
Lists It Appears On:
“Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts.
Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book.”
Lists It Appears On:
Aimed at high school and college students who need to take statistics to fulfill a degree requirement, this book follows a standard statistics curriculum with topics that include frequency distributions, probability, binomial distribution, poisson distribution, normal distribution, hypothesis testing, simple regression analysis, and more.
Lists It Appears On:
“What is a p-value Anyway? offers a fun introduction to the fundamental principles of statistics, presenting the essential concepts in thirty-four brief, enjoyable stories. Drawing on his experience as a medical researcher, Vickers blends insightful explanations and humor, with minimal math, to help readers understand and interpret the statistics they read every day.
Describing data; Data distributions; Variation of study results: confidence intervals; Hypothesis testing; Regression and decision making; Some common statistical errors, and what they teach us”
Lists It Appears On:
If you have ever looked for P-values by shopping at P mart, tried to watch the Bernoulli Trails on “People’s Court,” or think that the standard deviation is a criminal offense in six states, then you need The Cartoon Guide to Statistics to put you on the road to statistical literacy. The Cartoon Guide to Statistics covers all the central ideas of modern statistics: the summary and display of data, probability in gambling and medicine, random variables, Bernoulli Trials, the Central Limit Theorem, hypothesis testing, confidence interval estimation, and much more–all explained in simple, clear, and yes, funny illustrations. Never again will you order the Poisson Distribution in a French restaurant!
Lists It Appears On:
“Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.”
Lists It Appears On:
“Wouldn’t it be great if there were a statistics book that made histograms, probability distributions, and chi square analysis more enjoyable than going to the dentist? Head First Statistics brings this typically dry subject to life, teaching you everything you want and need to know about statistics through engaging, interactive, and thought-provoking material, full of puzzles, stories, quizzes, visual aids, and real-world examples.
Whether you’re a student, a professional, or just curious about statistical analysis, Head First’s brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it’s important; learn how to calculate probability and expectation; and much more.
“
Lists It Appears On:
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods.
Lists It Appears On:
This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. Using the author’s own data and examples from published research and the popular media, the book is a straightforward and accessible guide to statistics.
Lists It Appears On:
Renowned for its clear prose and no-nonsense emphasis on core concepts, Statistics covers fundamentals using real examples to illustrate the techniques.
| # | Book | Author | Lists |
| (Titles Appear On 1 List Each) | |||
| 22 | 5 Steps to a 5 AP Statistics, 2014-2015 Edition (5 Steps to a 5 on the Advanced Placement Examinations Series) | Duane Hinders | Stat Trek |
| 23 | 50 Challenging Problems in Probability with Solutions | Mosteller | Count Bayesie |
| 24 | A Course in Probability Theory | Kai Lai Chung | Big Data Made Simple |
| 25 | A First Look at Rigorous Probability Theory | Rosenthal | Count Bayesie |
| 26 | Against the Gods: The Remarkable Story of Risk | Peter L. Bernstein | Goodreads |
| 27 | An Introduction to Optimal Designs for Social and Biomedical Research | Berger MPF, Wong WK | Lab Stats |
| 28 | An Introduction to Probability Theory and Its Applications | William Feller | Big Data Made Simple |
| 29 | Analysis and Adjustment of Survey Measurements | Mikhail E.M. and Gracie G | Sanfoundry |
| 30 | Antifragile: Things That Gain from Disorder | Nassim Nicholas Taleb | Goodreads |
| 31 | Applied Multivariate Statistical Analysis | Richard A. Johnson | Goodreads |
| 32 | Applied Predictive Modeling | Max Kuhn | Goodreads |
| 33 | Applied Statistics and Probability for Engineers | Douglas C. Montgomery | Sanfoundry |
| 34 | Barron’s AP Statistics | Martin Sternstein Ph.D. | Wall Street Mojo |
| 35 | Baseball Between the Numbers: Why Everything You Know About the Game Is Wrong | Jonah Keri | Flashlight Worthy |
| 36 | Bayesian Data Analysis | Andrew Gelman | Goodreads |
| 37 | Bundle of Algorithms in Java, Third Edition, Parts 1-5: Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms | Robert Sedgewick | Big Data Made Simple |
| 38 | Business Statistics – 5th Edition- | Douglas Downing Ph.D. and Jeffrey Clark Ph.D. | Econ Guru |
| 39 | Calculus Made Easy | Thompson, Gardner | Count Bayesie |
| 40 | Causality: Models, Reasoning, and Inference | Judea Pearl | Goodreads |
| 41 | Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference | Shipley B | Lab Stats |
| 42 | Choice and Chance | Whitworth | Count Bayesie |
| 43 | CliffsNotes Statistics Quick Review, 2nd Edition | Scott Adams, Peter Z Orton, David H Voelker | Stat Trek |
| 44 | CliffsQuickReview Statistics | David H. Voelker, Peter Z. Orton, Scott Adams | Stat Trek |
| 45 | Contributions to a General Asymptotik Statistical Theory | Pfanzagl Wefelmeyer | Sanfoundry |
| 46 | Control Theoretic Splines: Optimal Control, Statistics, and Path Planning | Clyde Martin | Sanfoundry |
| 47 | Data Analysis with Open Source Tools | Janert | Count Bayesie |
| 48 | Data Analysis: a Bayesian Tutorial | Sivia, Skilling | Count Bayesie |
| 49 | Data Analysis: A Model Comparison Approach | Judd CM, et al. | Lab Stats |
| 50 | Data Mining: Practical Machine Learning Tools and Techniques | Ian H. Witten | Big Data Made Simple |
| 51 | Dataclysm: Who We Are | Christian Rudder | Goodreads |
| 52 | Digital Dice: Computational Solutions to Practical Probability Problems | Nahin | Count Bayesie |
| 53 | Discovering Statistics Using SPSS | Andy Field | Goodreads |
| 54 | Elements of Statistical Learning | Hastie, Tibshirani | Count Bayesie |
| 55 | Envisioning Information | Edward R. Tufte | Goodreads |
| 56 | Essentials of Statistics for Business and Economics | David R. Anderson, Dennis J. Sweeney and Thomas A. Williams | Econ Guru |
| 57 | Experimental Design for Biologists | Glass DJ | Lab Stats |
| 58 | Fifty Challenging Problems in Probability with Solutions | Frederick Mosteller | Big Data Made Simple |
| 59 | First Course in Probability | Sheldon Ross | Big Data Made Simple |
| 60 | Fooled | Nassim Nicholas Taleb | Goodreads |
| 61 | Forgotten Statistics: A Refresher Course with Applications to Economics and Business | Douglas Downing Ph.D., Jeff Clark Ph.D. | Stat Trek |
| 62 | Freakonomics: A Rogue Economist Explores the Hidden Side of Everything | Steven D. Levitt | Goodreads |
| 63 | ggplot2: Elegant Graphics for Data Analysis | Hadley Wickham | Goodreads |
| 64 | How animals work | Knut Schmidt-Nielsen | Five Books |
| 65 | How to talk so kids will listen and listen so kids will talk | Adele Faber and Elaine Mazlish | Five Books |
| 66 | Information Theory, Inference and Learning Algorithms | David J. C. MacKay | Wizzley |
| 67 | Intermediate Statistics For Dummies | Deborah J. Rumsey | Stat Trek |
| 68 | Introduction to Algorithms | Thomas H. Cormen | Big Data Made Simple |
| 69 | Introduction to Mathematical Statistics | Hogg and Craig | Wizzley |
| 70 | Introduction to Meta-Analysis | Borenstein M et al. | Lab Stats |
| 71 | Introduction to Probability | Dimitri P. Bertsekas | Big Data Made Simple |
| 72 | Introduction to Probability Theory | Paul G. Hoel | Big Data Made Simple |
| 73 | Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control | James Spall | Sanfoundry |
| 74 | Introductory Statistics for Business and Economics – 4th Edition – | Thomas H. Wonnacott and Ronald J. Wonnacott | Econ Guru |
| 75 | Judgment under uncertainty: heuristics and biases | Daniel Kahneman, Paul Slovic, and Amos Tversky | Five Books |
| 76 | Kaplan AP Statistics 2014 (Kaplan Test Prep) | Bruce Simmons, Mary Jean Bland, Barbara Wojciechowski | Stat Trek |
| 77 | Machine Learning: A Probabilistic Perspective | Murphy | Count Bayesie |
| 78 | Mathematical Statistics and Data Analysis | John A. Rice | Wizzley |
| 79 | MBA Fundamentals Statistics | Paul W. Thurman | Econ Guru |
| 80 | Model Based Inference in the Life Sciences: A Primer on Evidence | Anderson DR | Lab Stats |
| 81 | Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach | Burnham KP, Anderson DR | Lab Stats |
| 82 | Mostly Harmless Econometrics: An Empiricist’s Companion | Joshua D. Angrist | Goodreads |
| 83 | Numbers Is the New Way to Be Smart | Goodreads | |
| 84 | Numbers Rule Your World: The Hidden Influence of Probabilities and Statistics on Everything You Do | Kaiser Fung | Goodreads |
| 85 | OpenIntro Statistics: Third Edition | David M Diez | Wall Street Mojo |
| 86 | Outliers: The Story of Success | Malcolm Gladwell | Goodreads |
| 87 | Pattern Recognition and Machine Learning | Christopher M. Bishop | Goodreads |
| 88 | Practical Statistics Simply Explained (Dover Books on Mathematics) | Russell Langley | Stat Trek |
| 89 | Principles of Mathematical Analysis | Rudin | Count Bayesie |
| 90 | Probabilistic Graphical Models | Koller, Friedman | Count Bayesie |
| 91 | Probability & Measure Theory | Ash | Count Bayesie |
| 92 | Probability and Statistical Models: Foundations for Problems in Reliability and Financial Mathematics | Arjun K Gupta | Sanfoundry |
| 93 | Probability and Statistics | Morris H. DeGroot | Big Data Made Simple |
| 94 | Probability and Statistics for Engineers | Little R.E. | Sanfoundry |
| 95 | Probability Concepts in Engineering Planning and Design | Ang H.S. and Tang W.H. | Sanfoundry |
| 96 | Probability Statistics and Decision for Civil Engineers | Benjamin J.R. and Cornell C.A. | Sanfoundry |
| 97 | Probability Theory: A Concise Course (Dover Books on Mathematics) | Y.A. Rozanov | Big Data Made Simple |
| 98 | Probability: The Logic of Science | Jaynes | Count Bayesie |
| 99 | Randomness: The Hidden Role of Chance in Life and in the Markets | Goodreads | |
| 100 | Schaums Outline of Statistics, Fourth Edition (Schaum’s Outline Series) | Murray Spiegel, Larry Stephens | Stat Trek |
| 101 | Statistical Analysis with Excel For Dummies (For Dummies (Computers)) | Joseph Schmuller | Stat Trek |
| 102 | Statistical Decision Theory and Bayesian Analysis | James O. Berger | Wizzley |
| 103 | Statistics Explained: A Guide for Social Science Students, 2nd Edition | Perry R. Hinton | Stat Trek |
| 104 | Statistics for Business and Economics (12th Edition) | James T. McClave | Wall Street Mojo |
| 105 | Statistics for Experimenters: Design, Innovation, and Discovery | George E.P. Box | Goodreads |
| 106 | Statistics for People Who (Think They) Hate Statistics, 4th | Neil J. Salkind | Stat Trek |
| 107 | Statistics for the Utterly Confused, 2nd edition | Lloyd Jaisingh | Stat Trek |
| 108 | Statistics Hacks: Tips & Tools for Measuring the World and Beating the Odds | Bruce Frey | Stat Trek |
| 109 | Statistics II for Dummies | Deborah J. Rumsey | Stat Trek |
| 110 | Statistics Workbook For Dummies | Deborah J. Rumsey | Stat Trek |
| 111 | Statistics: A Very Short Introduction | David J. Hand | Goodreads |
| 112 | Statistics: Methods and Applications | Thomas Hill, Paul Lewicki | Stat Trek |
| 113 | Stochastic Differential Games. Theory and Applications | Chris P Tsokos Kandethody M Ramachandran Tsokos | Sanfoundry |
| 114 | Student Study Guide to Accompany Statistics Alive! | Wendy J. Steinberg | Wall Street Mojo |
| 115 | Super Crunchers: Why Thinking- | Ian Ayres | Goodreads |
| 116 | Superforecasting: The Art and Science of Prediction | Philip E. Tetlock | Goodreads |
| 117 | SuperFreakonomics: Global Cooling, Patriotic Prostitutes And Why Suicide Bombers Should Buy Life Insurance | Steven D. Levitt | Goodreads |
| 118 | Teaching Statistics Using Baseball | Jim Albert | Stat Trek |
| 119 | The Art of R Programming: A Tour of Statistical Software Design | Norman Matloff | Goodreads |
| 120 | The Bad Guys Won! | Jeff Pearlman | Flashlight Worthy |
| 121 | The Bill James baseball abstracts, from 1982 to 1986 | Five Books | |
| 122 | The Black Swan: The Impact of the Highly Improbable | Nassim Nicholas Taleb | Goodreads |
| 123 | The Book: Playing the Percentages in Baseball | Andrew Dolphin, Mitchel Lichtman, Tom M. Tango, foreword | Flashlight Worthy |
| 124 | The Drunkard’s Walk: How Randomness Rules Our Lives | Leonard Mlodinow | Goodreads |
| 125 | The Elements of Graphing Data | Cleveland WS | Lab Stats |
| 126 | The Elements of Statistical Learning: Data Mining, Inference, and Prediction | Trevor Hastie | Goodreads |
| 127 | The honest rainmaker | A. J. Liebling | Five Books |
| 128 | The Humongous Book of Statistics Problems | W. Michael Kelley, Robert A. Donnelly | Stat Trek |
| 129 | The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day | David J. Hand | Goodreads |
| 130 | The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century | David Salsburg | Goodreads |
| 131 | The Manga Guide to Statistics | Shin Takahashi, Ltd. Trend-Pro Co. | Stat Trek |
| 132 | The Numbers Game: Baseball’s Lifelong Fascination with Statistics | Alan Schwarz | Flashlight Worthy |
| 133 | The Probability Tutoring Book: An Intuitive Course for Engineers and Scientists (and Everyone Else!) | Carol Ash | Big Data Made Simple |
| 134 | The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t | Nate Silver | Goodreads |
| 135 | The Tao of Statistics: A Path to Understanding (With No Math) | Dana K. Keller | Stat Trek |
| 136 | The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy | Sharon Bertsch McGrayne | Goodreads |
| 137 | The Visual Display of Quantitative Information | Edward R. Tufte | Goodreads |
| 138 | Think Stats | Allen B. Downey | Goodreads |
| 139 | Thinking Statistically | Uri Bram | Goodreads |
| 140 | Thinking, Fast and Slow | Daniel Kahneman | Goodreads |
| 141 | Understandable Statistics | Charles Henry Brase, Corrinne Pellillo Brase | Stat Trek |
| 142 | Understanding Analysis | Abbott | Count Bayesie |
| 143 | Understanding Probability: Chance Rules in Everyday Life | Henk Tijms | Big Data Made Simple |
| 144 | Visualizing Data | Cleveland WS | Lab Stats |
| 145 | Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy | Cathy O’Neil | Goodreads |
| Source | Article |
| Big Data Made Simple | 15 best books to learn Probability |
| Count Bayesie | Count Bayesie’s Recommended Books in Probability and Statistics |
| Econ Guru | Top 5 Best Economic Statistics Books Reviewed |
| Five Books | Andrew Gelman recommends the best books on Statistics |
| Flashlight Worthy | Baseball By the Numbers: The Best Books on Baseball Stats |
| Goodreads | Popular Statistics Books |
| Lab Stats | Statistics for Experimental Biologists |
| Sanfoundry | Best Reference Books – Applied Statistics |
| Stat Trek | Statistics |
| Wall Street Mojo | TOP 11 BEST STATISTICS BOOKS |
| Wizzley | Statistics Book Reviews – Best Textbooks for Stats |
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