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A Student's Guide to Data and Error Analysis

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About the Authors
  • All students taking laboratory courses within the physical sciences and engineering will benefit from this book, whilst researchers will find it an invaluable reference. This concise, practical guide brings the reader up-to-speed on the proper handling and presentation of scientific data and its inaccuracies. It covers all the vital topics with practical guidelines, computer programs (in Python), and recipes for handling experimental errors and reporting experimental data. In addition to the essentials, it also provides further background material for advanced readers who want to understand how the methods work. Plenty of examples, exercises and solutions are provided to aid and test understanding, whilst useful data, tables and formulas are compiled in a handy section for easy reference.

    • A concise guide to bring students up-to-speed quickly
    • Provides the essential equations and methods that students need to handle and present scientific data
    • Includes data sets, units and additional background information in the appendices
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    Reviews & endorsements

    "Overall, this would be a nice text or reference to accompany a short course in statistics for undergraduate science or engineering..also useful for researchers desiring a primer or review...Recommended." - CHOICE

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    Product details

    • Date Published: May 2011
    • format: Adobe eBook Reader
    • isbn: 9781139064927
    • contains: 47 b/w illus. 12 tables 49 exercises
    • availability: Adobe Reader ebooks available from
  • Table of Contents

    Part I. Data and Error Analysis:
    1. Introduction
    2. The presentation of physical quantities with their inaccuracies
    3. Errors: classification and propagation
    4. Probability distributions
    5. Processing of experimental data
    6. Graphical handling of data with errors
    7. Fitting functions to data
    8. Back to Bayes: knowledge as a probability distribution
    Answers to exercises
    Part II. Appendices: A1. Combining uncertainties
    A2. Systematic deviations due to random errors
    A3. Characteristic function
    A4. From binomial to normal distributions
    A5. Central limit theorem
    A6. Estimation of the varience
    A7. Standard deviation of the mean
    A8. Weight factors when variances are not equal
    A9. Least squares fitting
    Part III. Python Codes
    Part IV. Scientific Data: Chi-squared distribution
    Normal distribution
    Physical constants
    Probability distributions
    Student's t-distribution

  • Instructors have used or reviewed this title for the following courses

    • Chemical Engineering Lab
    • Comparative Biomedical Imaging
    • Data Analysis and model Estimation
    • Introductory physics sequence with lab
    • Life at low Reynolds number
    • Materials Laboratory 2: Processing
    • Measurements Laboratory ll
    • Measurements and Instrumentation
    • Microclimatology Instrumentation
    • Physical Chemistry I and ll lab
    • Research Methods for Sciences
    • Statistics and Data Analysis in Geology
  • Author

    Herman J. C. Berendsen, Rijksuniversiteit Groningen, The Netherlands
    Herman Berendsen is Emeritus Professor of Physical Chemistry at the University of Groningen, the Netherlands. His research started in nuclear magnetic resonance but focused later on molecular dynamics simulations on systems of biological interest. He is one of the pioneers in this field and, with over 35,000 citations, is one of the most quoted authors in physics and chemistry. He has taught courses in molecular modeling worldwide and authored the book Simulating the Physical World (Cambridge, 2007).

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