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Direct measurement in the absence of systematic errors

The first step consists in evaluating the uncertainty on a quantity measured directly. The most common likelihoods which describe the observed values are the Gaussian, the binomial and the Poisson distributions.
Gaussian:
This is the well-known case of `normally' distributed errors. For simplicity, we will only consider $ \sigma$ independent of $ \mu$ (constant r.m.s. error within the range of measurability), but there is no difficulty of principle in treating the general case. The following cases will be analysed:
Binomial:
This distribution is important for efficiencies and, in the general case, for making inferences on unknown proportions. The cases considered include (see Section [*]):
Poisson:
The cases of counting experiments here considered2.15 are (see Section [*]):

next up previous contents
Next: Indirect measurements Up: Evaluation of uncertainty: general Previous: Evaluation of uncertainty: general   Contents
Giulio D'Agostini 2003-05-15