| Preface | p. xi |
| Experimental errors | p. 1 |
| Why do we do experiments? | p. 1 |
| Why estimate errors? | p. 1 |
| Random and systematic errors | p. 3 |
| The meaning of [sigma] | p. 7 |
| Distributions | p. 7 |
| Mean and variance | p. 9 |
| Gaussian distribution | p. 13 |
| Combining errors | p. 16 |
| Linear situations | p. 16 |
| Non-linear situations | p. 23 |
| Combining results of different experiments | p. 25 |
| Probability and statistics | p. 30 |
| Probability | p. 30 |
| Rules of probability | p. 32 |
| Statistics | p. 35 |
| Distributions | p. 39 |
| Binomial | p. 39 |
| Poisson | p. 41 |
| Gaussian distribution | p. 47 |
| Gaussian distribution in two variables | p. 53 |
| Using the error matrix | p. 62 |
| Specific examples of error matrix manipulations | p. 65 |
| Parameter fitting and hypothesis testing | p. 74 |
| Normalisation | p. 75 |
| Interpretation of estimates | p. 77 |
| Meaning of error estimates | p. 77 |
| Upper limits | p. 78 |
| Estimates outside the physical range | p. 80 |
| The method of moments | p. 81 |
| Maximum likelihood method | p. 85 |
| What is it? Some simple examples | p. 85 |
| The logarithm of the likelihood function, and error estimates | p. 88 |
| Comments on the maximum likelihood method | p. 91 |
| Several parameters | p. 96 |
| Extended maximum likelihood method | p. 98 |
| Least squares | p. 102 |
| What is it? | p. 102 |
| Notes on the least squares method | p. 106 |
| Correlated errors for y[subscript i] | p. 109 |
| Hypothesis testing | p. 111 |
| Use of weighted sum of squared deviations | p. 111 |
| Types of hypothesis testing | p. 115 |
| Minimisation | p. 120 |
| Detailed examples of fitting procedures | p. 125 |
| Least squares fitting | p. 125 |
| Function linear in parameters - the straight line | p. 125 |
| General least squares case | p. 133 |
| Straight line fit to points with errors on x and y | p. 137 |
| Kinematic fitting | p. 142 |
| General comments | p. 142 |
| Actual fit in a simplified case | p. 144 |
| Fit involving unmeasured variables | p. 147 |
| Maximum likelihood determination of Breit-Wigner parameters | p. 152 |
| Monte Carlo calculations | p. 161 |
| Introduction | p. 161 |
| What is it? | p. 161 |
| Random numbers | p. 162 |
| Why do Monte Carlo calculations? | p. 163 |
| Accuracy of Monte Carlo calculations | p. 164 |
| More than one dimension | p. 167 |
| An integral by Monte Carlo methods | p. 168 |
| Very simple applications of Monte Carlo calculations | p. 171 |
| Evaluation of [pi] | p. 171 |
| Mean transverse momentum | p. 172 |
| Non-uniform distributions and correlated variables | p. 174 |
| Typical uses of the Monte Carlo technique | p. 181 |
| Designing experiments | p. 181 |
| Testing programs | p. 183 |
| Contamination estimates | p. 184 |
| Geometrical correction factors | p. 185 |
| Do theory and experiment agree? | p. 185 |
| Resonance or statistical fluctuation? | p. 186 |
| Phase space distributions | p. 189 |
| Matrix elements | p. 190 |
| Parameter determination | p. 190 |
| Detailed examples | p. 191 |
| Geometrical correction factors | p. 191 |
| Physics | p. 197 |
| Non-physics applications | p. 200 |
| How to save yourself a fortune | p. 200 |
| The Garden of Eden problem | p. 200 |
| Background subtraction procedures | p. 204 |
| The number of constraints in kinematic fitting | p. 211 |
| Bibliography | p. 223 |
| Index | p. 224 |
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