Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea/ Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B

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Siddharth Krishna is a post-doctoral researcher at Microsoft Research Cambridge, UK. He completed his Ph.D. in logic and verification at New York University, where he had the good fortune to work with Nisarg, Dennis, and Thomas. He now works on parallel and distributed algorithms for large-scale machine learning workloads. When he is not pleading with computers to do his bidding, he likes to watch comedy panel/news shows and play ultimate frisbee.Nisarg Patel is a Ph.D. student at New York University's Department of Computer Science, where he works with Siddharth, Dennis, and Thomas on automated verification of concurrent programs. His academic interests also include synthesis of controller programs for robots. Outside of computer science, he loves playing football and reading about history and politics.Dennis Shasha is a Julius Silver Professor of computer science at the Courant Institute of New York University and an Associate Director of NYU Wireless. In addition tohis long fascination with concurrent algorithms, he works on meta-algorithms for machine learning to achieve guaranteed correctness rates; with biologists on pattern discovery for network inference; with physicists and financial people on algorithms for time series; on database tuning; and tree and graph matching. Because he likes to type, he has written six books of puzzles about a mathematical detective named Dr. Ecco, a biography about great computer scientists, and a book about the future of computing. He has also written technical books about database tuning, biological pattern recognition, time series, DNA computing, resampling statistics, and causal inference in molecular networks. He has written the puzzle column for various publications including Scientific American, Dr. Dobb's Journal, and currently the Communications of the ACM. He is a fellow of the ACM and an INRIA International Chair.Thomas Wies is an Associate Professor in computer science at the Courant Institute of New York University and a member of the Analysis of Computer Systems Group. His research interests are in programming languages and formal methods with a focus on program analysis and verification, automated deduction, and correctness of concurrent software. He is the recipient of an NSF CAREER Award and has won multiple best paper awards. His fascination with concurrent tree traversals extends to his spare time: he enjoys hikes in the woods.

Inhalt
The Basic Idea.- Pragmatic Considerations when Using Resampling.- Terminology.- The Essential Stats.- Case Study: New Mexico's 2004 Presidential Ballots.- References.- Bias Corrected Confidence Intervals.- Appendix B.