Contents

pp vii-x

Preface

pp xi-xii

Part I - Data and error analysis

pp 1-2

1 - Introduction

pp 3-4

2 - The presentation of physical quantities with their inaccuracies

pp 5-17

3 - Errors: classification and propagation

pp 18-26

4 - Probability distributions

pp 27-52

5 - Processing of experimental data

pp 53-70

6 - Graphical handling of data with errors

pp 71-83

7 - Fitting functions to data

pp 84-110

8 - Back to Bayes: knowledge as a probability distribution

pp 111-122

References

pp 123-124

Answers to exercises

pp 125-132

Part II - Appendices

pp 133-134

A1 - Combining uncertainties

pp 135-137

A2 - Systematic deviations due to random errors

pp 138-140

A3 - Characteristic function

pp 141-142

A4 - From binomial to normal distributions

pp 143-147

A5 - Central limit theorem

pp 148-150

A6 - Estimation of the variance

pp 151-153

A7 - Standard deviation of the mean

pp 154-157

A8 - Weight factors when variances are not equal

pp 158-159

A9 - Least-squares fitting

pp 160-166

Part III - Python codes

pp 167-196

Part IV - Scientific data

pp 197-219

Index

pp 220-225