The application of geometry to statistical theory and practice has produced a number of different approaches. The first is the application of differential geometry to statistics, which is often called Information Geometry. It largely focuses on typically multivariate, invariant and higher-order asymptotic results in full and curved exponential families through the use of differential geometry and tensor analysis. Also included in this approach are consideration of curvature, dimension reduction and information loss.