Overfitting appears to be a common problem in inverse force estimation, which leads to forces that have erroneously high amplitudes. As such, overfitting negatively impacts the predictive capability of the transfer path analysis (TPA) model, especially in component-based TPA. Sparse inverse solutions have recently been shown as a potential method for mitigating these overfitting errors when traditional methods (i.e., Tikhonov regularization and singular value truncation) fail. Overfitting is reduced via the variable selection feature of the sparse solution, where “inappropriate” inputs (which contribute to overfitting) are identified and eliminated from the inverse problem. However, sparse solutions come at the cost of apparent nonphysicality in the estimated forces (where forces are activated/deactivated on a frequency-by-frequency basis), leading to suspicions about the applicability of the methods. This paper will discuss the apparent nonphysicality inherent to sparse inverse solutions while making comparisons to how the traditional methods also modify the inverse source-estimation problem in potentially nonphysical ways. Further, the advantages and disadvantages of the different inverse methods will be discussed to help understand the applicability of sparse inverse solutions in TPA.