A new preferred point geometric structure for statistical analysis, closely related to Amari's alpha-geometries, is introduced. The added preferred point structure is seen to resolve the problem that divergence measures do not obey the intuitively natural axioms for a distance function as commonly used in geometry. Using this tool, two key results of Amari which connect geodesics and divergence functions are developed. The embedding properties of the Kullback-Leibler divergence are considered and a strong curvature condition is produced under which it agrees with a statistically natural (squared) preferred point geodesic distance. When this condition fails the choice of divergence may be crucial. Further, Amari's Pythagorean result is shown...