Book contents
- Frontmatter
- Dedication
- Contents
- For the student
- For the instructor
- 1 Science and statistical data analysis
- 2 Statistical summaries of data
- 3 Simple statistical inferences
- 4 Probability theory
- 5 Random variables
- 6 Estimation and maximum likelihood
- 7 Significance tests and confidence intervals
- 8 Monte Carlo methods
- Appendix A Getting started with statistical computation
- Appendix B Data case studies
- Appendix C Combinations and permutations
- Appendix D More on confidence intervals
- Appendix E Glossary
- Appendix F Notation
- References
- Index
For the student
Published online by Cambridge University Press: 05 June 2014
- Frontmatter
- Dedication
- Contents
- For the student
- For the instructor
- 1 Science and statistical data analysis
- 2 Statistical summaries of data
- 3 Simple statistical inferences
- 4 Probability theory
- 5 Random variables
- 6 Estimation and maximum likelihood
- 7 Significance tests and confidence intervals
- 8 Monte Carlo methods
- Appendix A Getting started with statistical computation
- Appendix B Data case studies
- Appendix C Combinations and permutations
- Appendix D More on confidence intervals
- Appendix E Glossary
- Appendix F Notation
- References
- Index
Summary
Science is not about certainty, it is about dealing rigorously with uncertainty. The tools for this are statistical. Statistics and data analysis are therefore an essential part of the scientific method and modern scientific practice, yet most students of physical science get little explicit training in statistical practice beyond basic error handling. The aim of this book is to provide the student with both the knowledge and the practical experience to begin analysing new scientific data, to allow progress to more advanced methods and to gain a more statistically literate approach to interpreting the constant flow of data provided by modern life.
More specifically, if you work through the book you should be able to accomplish the following.
• Explain aspects of the scientific method, types of logical reasoning and data analysis, and be able to critically analyse statistical and scientific arguments.
• Calculate and interpret common quantitative and graphical statistical summaries.
• Use and interpret the results of common statistical tests for difference and association, and straight line fitting.
• Use the calculus of probability to manipulate basic probability functions.
• Apply and interpret model fitting, using e.g. least squares, maximum likelihood.
• Evaluate and interpret confidence intervals and significance tests.
Students have asked me whether this is a book about statistics or data analysis or statistical computing. My answer is that they are so closely connected it is difficult to untangle them, and so this book covers areas of all three.
- Type
- Chapter
- Information
- Scientific InferenceLearning from Data, pp. x - xiPublisher: Cambridge University PressPrint publication year: 2013