General strategies for the analysis of ringing data

Abstract

A general, consistent strategy for data analysis is outlined, based on information and likelihood theory. A priori considerations lead to the definition of a set of candidate models, simple criteria are useful in ranking and calibrating the models based on estimates of (relative) Kullback-Leibler information, inference can be based on either the best model or a weighted average of several models. Model selection uncertainty can be quantified and should be incorporated into estimators of precision. Some comments are offered on statistical hypothesis testing and data dredging.