Orig inal abstract of the ``primer''

Bayesian statistics is based on the subjective definition of probability as ``degree of belief'' and on Bayes' theorem, the basic tool for assigning probabilities to hypotheses combining a priori judgements and experimental information. This was the original point of view of Bayes, Bernoulli, Gauss, Laplace, etc. and contrasts with later ``conventional'' (pseudo-)definitions of probabilities, which implicitly presuppose the concept of probability. These notes^3.1 show that the Bayesian approach is the natural one for data analysis in the most general sense, and for assigning uncertainties to the results of physical measurements - while at the same time resolving philosophical aspects of the problem. The approach, although little known and usually misunderstood among the high energy physics community, has become the standard way of reasoning in several fields of research and has recently been adopted by the international metrology organizations in their recommendations for assessing measurement uncertainty.

These notes describe a general model for treating uncertainties originating from random and systematic errors in a consistent way and include examples of applications of the model in high energy physics, e.g. ``confidence intervals'' in different contexts, upper/lower limits, treatment of ``systematic errors'', hypothesis tests and unfolding.