As defined (for example, in Wikipedia):
Completeness is a property of a statistic in relation to a model for a set of observed data. In essence, it is a condition which ensures that the parameters of the probability distribution representing the model can all be estimated on the basis of the statistic: it ensures that the distributions corresponding to different values of the parameters are distinct.
A sufficient statistic is minimal sufficient if it can be represented as a function of any other sufficient statistic. Intuitively, a minimal sufficient statistic most efficiently captures all possible information about the parameter θ.
What I am looking for is a way to visualize either of these two explanations (intuitions?) in some way.
Any suggestions on how that might be done?
