This paper addresses one of the critical questions of scientific inquiry: How do we know when a given data set is representative of the phenomenon being examined? For paleomagnetists, the question is often whether a particular dataset sufficiently averaged paleosecular variation (PSV). To this aim, we updated an existing PSV dataset that now comprises 2441 site mean directions from 94 individual studies (PSV10-24). Minimal filtering for data quality resulted in 1619 sites from 90 publications. Fitting PSV10-24 with three newly defined parameters as well as two existing ones form the basis of a Giant Gaussian Process field model (THG24) consistent with the data. Drawing directions from THG24 yields directional distributions predicted for a given latitude allowing a comparison between empirical distributions and the cumulative distribution function generated by the model. This tests whether the observed data adequately averaged out PSV according to THG24. Sedimentary datasets that may have experienced inclination shallowing can be corrected using an (un)flattening factor that yields directions satisfying THG24 in a newly-defined, four-parameter space. This approach builds on the Elongation-Inclination (E/I) method of Tauxe and Kent (2004), so the approach introduced here is called SVEI. We show examples of the use of SVEI and explain how to use this newly developed python code that is publicly available in the PmagPy GitHub repository.