  Statistical power estimation for studies with multiple model parameters is
inherently a multivariate problem. Power for individual parameters of interest
cannot be reliably estimated univariately since correlation and variance
explained relative to one parameter will impact the power for another
parameter, all usual univariate considerations being equal. Explicit solutions
in such cases, especially for models with many parameters, are either
impractical or impossible to solve, leaving researchers to the prevailing
method of simulating power. However, the point estimates for a vector of model
parameters are uncertain, and the impact of inaccuracy is unknown. In such
cases, sensitivity analysis is recommended such that multiple combinations of
possible observable parameter vectors are simulated to understand power
trade-offs. A limitation to this approach is that it is computationally
expensive to generate sufficient sensitivity combinations to accurately map the
power trade-off function in increasingly high-dimensional spaces for the models
that social scientists estimate. This paper explores the efficient estimation
and graphing of statistical power for a study over varying model parameter
combinations. We propose a simple and generalizable machine learning inspired
solution to cut the computational cost to less than 10% of the brute force
method while providing F1 scores above 90%. We further motivate the impact of
transfer learning in learning power manifolds across varying distributions.
