Measurements and their Uncertainties: A practical guide to modern error analysis

I recently reviewed 10 current books on data and error analysis for undergraduate physics majors, and recommend this one by Hughes and Hase. I was looking for a book that students can read and understand on their own, is concise and easily used as a quick reference, is inexpensive, and follows a relatively modern approach. Hughes and Hase meet these criteria. Their book is written in an easy to understand style that uses a "visual" approach to explain concepts and derive formulas (using many thumbnail-sized plots in the margins). I also like the topical sequence: Look at real data before delving into probability. The book takes good departures from tradition by only presenting addition of uncertainties in quadrature and by including a computational approach to uncertainty estimation based on spreadsheets. Missing concepts that I would like to see included are GUM (Guide to the Expression of Uncertainty in Measurement) and bootstrapping. This is the shortest (<150 pages) and least expensive ($35) of the available books.

I have to agree with the other review. The textbook is inexpensive, well written, filled with examples and graphs, and keeps the math derivations to the bare minimum needed. I like it better than Taylor, which is long, and takes forever to get to the point. Hughes will get to the point immediately. Each chapter is fairly short and nice to read. I don't generally like long chapters for this type of thing, since the concepts are themselves very simple, they should be taught in a simple way, as this book does. There are of course some shortcomings (topics we'd like them to expand on, minor typos), but overall, this is the best book available at this time to teach or learn this kind of material at the undergraduate level.

This book can be used for a good introduction to uncertainties in measurements on every level of expertise. Would definitely recommend it.

I'll see you on the other side of this one

This book is an excellent introduction to error analysis for 2nd/3nd year undergraduate students in science or technology. To understand the subject as presented in this book with enough depth, some prior knowledge of probablity theory, of descriptive statistics and of 2nd year level calculus are needed. The approach of the first 4 chapters is rather classical. The following chapters about data visualisation and reduction, least square fitting of complex functions, and computer minimalisation and the error matrx, give a good insight in the application of modern computer software in the field of error analysis. Chapter 8, about hypothesis testing, especially in connection with the chi-squared statistic will be rather difficult to follow for students that didn't follow an introductory statistics course. Though this chapter gives a good summery of the technics used for hypothesis testing by means of the chi-squared distribution. Chapter 9, topics for further study, mentions Monte Carlo and bootstrap methods and Bayesian inference, subjects you normally don't find in introductory courses in error analysis. In short: an excellent book for use in undergraduate studies

good

The book is good. I have a PDF version and all the figures and color boxes, conclusions, etc. are produced using good quality vector graphics. However, the copy I received comes from the AMAZON Printing Facility in San Bernardino, CA. It looks like a cheap photocopy, printed out with horrible quality. Since there is a lot of detail and math in this book it is necessary even for the text font to be printed in good quality. It looks as if the PDF was converted to JPEG format before printing it out, resulting in pixelated and/or blurry content. The plots, color boxes, and math fonts are the most affected by this. I won't bother returning it because most probably I will get another copy just as bad. I don't want to impact negatively the score of the book, or the reputation of the author. On the contrary, It would be good if the author did something so his work gets to the public the way it is supposed to.

But it does require some previous knowledge of statistics.

This is a very practical book showing simple methods to draw accurate conclusions from any analysis. It is especially useful in determining the precision of results of fits to linear and non-linear data in THE REAL WORLD. The methods show how 'fits' can really be checked and how to identify if you are missing something in your model. It also protects you from drawing overly precise conclusions from imprecise data. The only thing better would be to sit down with Ifan Hughes yourself. Every engineer should have a copy of this book.

The book is not for novice or amateurs. It requires having some background in the calculus. The book useful for those who want to go deeper in the topic. I see some assignments for each section; however, there are no answers to the questions that allow to verify the solutions. In spite of it I recommend the book for those who have good background in calculus and want to improve their understanding in the subject of measurements and their uncertainties.

I wanted to return this item since I can't even open it up. But no one got back to me lol.

A nice reference book to have for general data analysis, especially in the case of physical chemistry, chemical engineering, physics and etc... It really is an extensive guide to data analysis in a laboratory. Fast delivery

A very well written book. I bought it as it is the recommended.ended reading for a course I am undertakimg soon and I wanted to refresh my memory on the maths (its been a little while) The explanations are clear and the overall subject isn't as dauntig as I thought it was going to be.