This is a new Category on Herding Cats that comes from my finishing of the book Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics, Gary Smith.
Of late I've encountered many people on the web and in face-to-face encounters that simply fail to understand how probability and statistics works and how the principles of probability and statistics impact our decision-making processes.
These misunderstandings are encountered in everyday conversations, business settings, policy-making settings, and other engagements where numbers are involved.
So when you hear someone make some statement involving probability and statistics, ask by what principle are you supporting that conjecture?
Here are a few resources I've used for increasing my understanding of probability and statistics in my work and everyday life.
- How to Lie with Statistics, Darrell Huff - this is the first book you should have on your shelf, no matter what role you play at work or in life.
- Discover Probability: How to Use it, How to Avoid Misusing It, and How It Affects Every Aspect of Your Life, Arueh Be-Naim
- Chances Are: Adventures in Probability, Micael Kaplan and Ellen Kaplan
- Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference, Cameron Davidson-Pilon
- Reasoning about Uncertainty, Joseph Y. Halpern
- Predictability Irrational: The Hidden Forces That Shape Our Decisions, Dan Ariely
- How to Take a Chance, Darrell Huff and Irving Geis
- Dueling Idiot's and Other Probability Puzzles, Paul J. Nahin
- Probability and Statistics, Julius Blum and Judan Rosenblatt - this was a graduate school text that got me started on the path of applying probability and statistics to everything I do.
- Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, George E. P. Box, William Hunter, and J. Stuart Hunter - this is the book where Box states All Models are Wrong, Some are Useful. This is another grad school book that set me on my way to understanding how to apply statistics in decision making.
- Regression Analysis by Example, Samprit Chatterjee and Ali S. Hadi
- Applied Regression Analysis Third Edition, Norman Draper and Harry Smith
- Forecasting: Methods and Applications, Spyros Makridakis, Steven C. Wheelwright, and Rob J. Hyndman - in our program planning and controls domain ARIMA (Autoregressive Integrated Moving Average) is a powerful tool for forecasting future project behaviors based on past performance.
- How to Predict the Unpredictable: The Art of Outsmarting Almost Everyone, William Poundstone - this book is the practical side of Kahneman and Tversky's representativeness behavioral bias.
- Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Paul R. Garvey - this book is fundamental to managing cost and the supporting schedules on all the projects I work. Systems Engineering is the basis of all we do, so this book enables the execution of the programs driven by Systems Engineering.
- Practical Nonparametric Statistics, W. J. Conover - Nonparametric statistics is a statistical method where data is not required to fit a normal distribution. Nonparametric statistics uses data that is often ordinal, meaning it does not rely on numbers, but rather a ranking or order of sorts. This is found many times in project work around risk management and other performance measures that are ordinal rather than cardinal.
- Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics, Gary Smith - as a companion to How to Lie With Statistics, this books shows how we make fallacious decisions based on bad statistics.
- Guesstimation: Solving the World's Problem on the Back of a Cocktail Napkin, Lawrence Weinstein and John A. Adam
- Advanced Statistics DeMystified, Dr. Larry J. Stephens - this is a good introduction to applying statistics to engineering problems.
- Principles of Statistics, M. G. Bulmer - a classic statistic book
- The Practical Cheating Statistics Handbook: The Sequel (2nd Edition) - this is a handy book when you encounter the need to solve a problem without actually having to do the heavy work
- Flaws and Fallacies in Statistical Thinking, Stephen K. Campbell - we all make fallacious decisions based on statistics or know people who do. This is one of those mandatory reading books that will show why those fallacies exist and how to avoid them. Especially on our projects where uncertainty abounds.
- Statistics: A Very Short Introduction, David J. Hand - a handy book with summaries of all the principles.
- Probability, Statistics, and Queuing Theory with Computer Science Applications, Arnold O. Allen - this was a grad school text as well. Long before Kanban (a queuing method actually) that we took a course needed for performance management on a particle accelerator data flow from a remote site onto our data server over a slow dial-up line in the late 70's.
- Introduction to Stochastic Process, Paul G. Hoel, Sidney C. Port, Charles J. Stone - this is a graduate school text where I learned everything in the universe is a non-stationary stochastic process.
- The Art of Modeling Dynamic Systems: Forecasting for Chaos, Randomness and Determinism, Foster Morrison - when you hear we can't forecast the future, read this book to find out how that is a fallacy and how to do it with the proper limitations of available modeling tools and processes in our engineering domain.
- Introduction to Stochastic Models, Second Edition, Roe Goodman - another stochastical process book needs for managing project work in the presence of uncertainty
