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Probably Not: Future Prediction Using Probability and Statistical Inference

Lawrence N. Dworsky
John Wiley
Publication Date: 
Number of Pages: 
[Reviewed by
Sarah Boslaugh
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Probably Not is a book for people who would like to understand more about probability and statistics, who are not prepared to enroll in a college course in the subject, yet want more than a jokey, flyover treatment of the subject. It’s particularly good for people who will be consumers rather than producers of statistics, who need to understand what they mean but don’t need to do the calculations (or programming) themselves.

The ideal reader for Probably Not is someone with intellectual curiosity and reasonable comfort with math (at least through the high school level), who likes to think through problems and is willing and able to read a graph or follow a mathematical example in order to understand a larger point.

Dworsky mixes background material about probability and statistics with practical examples. For instance, chapters 1–4 introduce the principles of probability, probability distributions, and simple statistics including the mean and standard deviation. Chapter 5 demonstrates how those concepts apply in the field of insurance, including a lengthy explanation of life tables (well, maybe not lengthy for someone accustomed to using life tables, but perhaps a bit much for the casual reader). This chapter makes a good “audition” for the book: if you, or your students, are attracted by the method of presentation in this chapter, Probably Not is a good fit for you. If not, probably not.

One good use for Probably Not is as a supplemental textbook for an undergraduate statistics course. It’s not a replacement for a standard textbook, but supplies something missing from some textbooks: a larger context for all the technical material the students are expected to learn. It also challenges the reader to think about statistical concepts in a way which may reach some students (or general readers) who have difficulty connecting with a more technical approach. The fact that Dworsky uses examples from many fields, and discusses topics not usually covered in beginning courses, may also increase student interest in pursuing statistics at a more advanced level.

Lawrence N. Dworsky is a Fellow of the Institute of Electrical and Electronic Engineers and a former corporate research lab director at Motorola. He has held academic positions at Columbia University, Northern Illinois University and Florida Atlantic University. He has also served as a consultant to the Defense Advanced Research Projects Agency (DARPA), Littelfuse Corporation, and HRL Laboratories, LLC.

Sarah Boslaugh ( is a Performance Review Analyst for BJC HealthCare and an Adjunct Instructor in the Washington University School of Medicine, both in St. Louis, MO. Her books include An Intermediate Guide to SPSS Programming: Using Syntax for Data Management (Sage, 2004), Secondary Data Sources for Public Health: A Practical Guide (Cambridge, 2007), and Statistics in a Nutshell (O'Reilly, forthcoming), and she served as Editor-in-Chief for The Encyclopedia of Epidemiology (Sage, 2008).



1. An Introduction to Probability.

Predicting The Future.

Rule Making.

Random Events and Probability.

The Lottery {Very Improbable Events and Large Data Sets}.

Coin Flipping {Fair Games, Looking Backwards For Insight}.

The Coin Flip Strategy That Can’t Lose.

The Prize Behind The Door {Looking Backwards For Insight, Again}.

The Checker Board {Dealing With Only Part Of The Data Set}.

2. Probability Distribution Functions And Some Basics.

The Probability Distribution Function.

Averages And Weighted Averages.

Expected Values.

The Basic Coin Flip Game.

The Standard Deviation.

The Cumulative Distribution Function.

The Confidence Interval.

Final Points.

3. Building a Bell.

4. Random Walks.

The One Dimensional Random Walk.

What Probability Really Means.


5. Life Insurance and Social Security.

Insurance as Gambling.

Life Tables.

Birth Rates and Population Stability.

Life Tables, Again.


Social Security - Sooner Or Later?.

6. Binomial Probabilities.

The Binomial Probability Formula.

Permutations And Combinations.

Large Number Approximations.

The Poisson Distribution.

Disease Clusters.


7. Pseudorandom Numbers and Monte Carlo Simulations.

Pseudorandom Numbers.

The Middle Square PSNG.

The Linear Congruential PSNG.

A Normal Distribution Generator.

An Arbitrary Distribution Generator.

Monte Carlo Simulations.

A League Of Our Own.

8. Some Gambling Games In Detail.

The Basic Coin Flip Game.

The Gantt Chart.

The Ultimate "Winning Strategy".

The Game Show.

Parimutuel Betting.

9. Traffic Lights And Traffic.

Outsmarting A Traffic Light?.

Many Lights And Many Cars.

Simulating Traffic Flow - The Simulation.

Simulation Results.

10. Combined And Conditional Probabilities.

Functional Notation.

Conditional Probability.

Medical Test Results.

The Shared Birthday Problem.

11. Scheduling And Waiting.

Scheduling Appointments In The Doctor’s Office.

Lunch With A Friend.

Waiting For A Bus.

12. Stock Market Portfolios.

13. Benford, Parrondo and Simpson.

Benford’s Law.

Parrondo’s Paradox.

Simpson’s Paradox.

14. Networks, Infectious Disease Propagation and Chain Letters.

Degrees Of Separation.

Propagation Along Networks.

Some Other Uses Of Networks.

Neighborhood Chains.

15. Bird Counting.

A Walk In The Woods.

A Model Of Bird Flying Habits.

Spotting A Bird.

Putting It All Together.

16. Statistical Mechanics And Heat.

Statistical Mechanics.


17. Introduction To Statistical Analysis.


Sample Distributions and Standard Deviations.

Estimating Population Average From A Sample.

The Student-T Distribution.

Polling Statistics.

Did A Sample Come From A Given Population?.

18. Chaos and Quanta.


Probability In Quantum Mechanics.