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Rating: 
Summary: Excellent desktop reference
Review: As a professional engineer with a recurring need to crunch large amounts of statistical data, I find that this book is the perfect quick guide to things that forget and don't use that often. If has easy to follow language, and the best part about it is that I don't need to reread the whole thing to get a good explanation of a topic in the last chapter.I had to knock it down a star because it is a touch out of date. The math is fine, but I wish that there was a companion that explained how to do some of the more uncommon operations using common spreadsheeting or data analysis software. Sometimes, figuring out how to get MS Excel to do what Taylor recommends that I do can be more cumbersome than anything else. If nothing else, it has a great picture on the cover.
Rating: 
Summary: A little off the top, please...
Review: But how much is 'a little'? I first encountered this book when I was a physics and astronomy major in college, a major that changed over time to include mathematics proper, then political science, then other humanities such as religious studies, history and philosophy. Strange as it may seem, this text has been one of the few constants that has been helpful in almost every field. For physics and any of the natural sciences, the content of this book is highly necessary - be in chemistry, physics, astronomy, geology, or biology, all sciences depend upon observation and analysis, both of which are far from perfect. The task of ever-increasing observational and analytical precision is both an art and a science in and of itself, and one of the tasks of any scientist is to discover where errors might lie. Interestingly, this also occurs in political science and sociology, economics and history, and even philosophy (logic can incorporate ideas from error analysis, as can epistemology). Error analysis is primarily a statistical tool, and those who have had statistics will find this very familiar. The first part of the book is very simple - Taylor assumes no background, so gives an introduction to the simple reading of charts, graphs, scales and other such things, with plenty of examples. He talks about estimating, significant figures, fractional uncertainties, and how uncertainties can accumulate. How can 2 + 2 = 5? Well, if you round to the highest or lowest whole number, 2.49 and 2.49 will both be rounded down to 2 (under many normal rounding procedures), yet if the underlying calculation or data include the 'real' information, 2.49 + 2.49 in fact equals 4.98, very close to 5. If you think that's confusing, you ain't seen nothing yet... Taylor's first part concludes by looking at the basics of simple statistical analysis - standard deviations, normal distributions, justification of the mean as best estimate, and a brief introduction to the concept of confidence. Part two gets into more detailed analysis, including least-squares fitting, correlation coefficients, binomial distributions, Poission distributions, and the chi-squared test. The mathematics requirement goes up as the chapters progress - the early chapters only require an elementary knowledge of algeba; as the text continues, knowledge of differentiation, integration and exponential functions are necessary. A first-year course in calculus should be sufficient for easy understanding here; it is possible to get through the material without this background, but it will be more difficult. This text is designed to be a self-study for the students; it can be introduced in lectures prior to lab work, but can also be used easily for the independent reader to understand. This book is really intended for the physical scientist - most of the examples come from problems in optics or mechanics (physics problems). Useful, helpful, and a good introduction to error analysis. Read and understand.
Rating:
Summary: Excellent and simple
Review: Check out this really good book. It's a blindingly simple explaination of error analysis and measurement error. If more people knew basic lab science, there would have been a much more relevant discussion in November of 2000. The "fact" that George W. Bush "won" by 49 votes or 600 votes doesn't matter. They are basically equivalent statements given the technology and its error bound.
Rating:
Summary: A Classic
Review: I can't believe I'm the first person to review this. Everyone I work with loves this book, it's a classic.
Rating:
Summary: A Handy Reference
Review: I had to get this book because it was one of the texts required for an undergradute physics course in data analysis. It turns out to have been a very useful book. I've used it as reference, for among other things, analyzing data collected for a port development project in Pusan, South Korea, developing software used for medical diagnostics equipment, and, most recently, in developing financial software.
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Summary: Wow! This is the book of my dreams!
Review: I started reading this book, "An Introduction to Error Analysis", when I was a student and I haven't finished it yet! 34 years have passed... it's so amazing! The 8th chapter on the Minimum Squares Method is my favourite. Get it at once! Your life will never be the same after this fabulous experience.
Rating:
Summary: Easy-To-Read Text on Error Analysis
Review: Many undergraduate students in sciences and engineering must have encountered this experience: You conduct an experiment and collect the relevant data. You are asked to fit your data into a straight line by performing one or multiple linear regression. You are also to present any uncertainty and error in your data as well as calculation. You panic and scratch your head and don't know what's the appropriate procedure to carry out these analysis. Here comes John Taylor's "An Introduction to Error Analysis", which introduces the study of uncertainties to students. The book assumes no prior knowledge and uses a plethora of pertinent examples (drawn from chemistry, physics, and engineering) to illustrate topics like propagation of uncertainties, random uncertainties, rejection of data, least-squares fitting, and distribution. This book will save hours of studying and researching on error analysis method. It is very well-written and reader-friendly that lower division students will find it useful.
Rating:
Summary: Easy-To-Read Text on Error Analysis
Review: Many undergraduate students in sciences and engineering must have encountered this experience: You conduct an experiment and collect the relevant data. You are asked to fit your data into a straight line by performing one or multiple linear regression. You are also to present any uncertainty and error in your data as well as calculation. You panic and scratch your head and don't know what's the appropriate procedure to carry out these analysis. Here comes John Taylor's "An Introduction to Error Analysis", which introduces the study of uncertainties to students. The book assumes no prior knowledge and uses a plethora of pertinent examples (drawn from chemistry, physics, and engineering) to illustrate topics like propagation of uncertainties, random uncertainties, rejection of data, least-squares fitting, and distribution. This book will save hours of studying and researching on error analysis method. It is very well-written and reader-friendly that lower division students will find it useful.
Rating:
Summary: useful in biology too
Review: Students in our cell biology laboratory use this book frequently to help us analyze our data. For example there is an excellent discussion about the difference between standard error and standard error of the mean. While it is aimed at undergraduate physicists and engineers, it will be practical for (and easily digested by) biologists as well.
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Summary: A gentle introduction to data and error analysis
Review: Taylor's book is simply amazing. In little more than three hundred pages it manages to explain in a crystal clear manner concepts such as the propagation of errors (starting from simple cases and moving to the general treatment), the meaning of the standard deviation of the population, of the sample and of the mean, the maximum likelihood principle, hypothesis test and confidence levels, the chi squared test and the meaning of correlation.
True, this is not a textbook on mathematical statistic, so you won't find elaborate proofs here: much is left to the reader's intuition. But as the saying goes, 'is not a bug, it's a feature!'. This text makes you understand what all those books on statistics and probability are about (or at least some of their most important applications) and it does it so well that you will reach the end of each chapter asking yourself "oh, that was it?". Part of the book is devoted to application of error analysis and you will find chapters on weighted means, on the rejection of data, plus linear and nonlinear regression. The exercises are intriguing and all in all this is a very well written book. Even if you plan to study the matter deeper, on tougher textbooks, please consider preparing yourself to the tougher mathematical stuff by reading this wonderful book. You won't regret it. And possibly, you will come back to it from time to time.
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