Considerations of multicollinearity, also known as ill-conditioning, are essential to developing a successful fitting model. We speculate that the limited application of multidimensional fitting algorithms in nonlinear spectroscopy to date is due in part to overlooking this aspect of models. A fitting problem is said to be multicollinear (or illconditioned) if column vectors of the Jacobian 𝐉𝐉 are nearly linearly dependent, which causes instability in computing inverse matrices associated with 𝐉𝐉, such as the Hessian 𝛁𝛁𝛁𝛁𝛁𝛁 -1 and 𝐕𝐕 𝐩𝐩 . Consequently, the calculation of 𝚫𝚫𝐩𝐩 in Eq. 6 may be inaccurate or unstable (i.e. a major cause of stalling) and covariances among those nearly linearly dependent parameters become overwhelming. In more extreme cases of multicollinearity, tiny perturbations of noise cause wild fluctuations in fitting parameters, and error estimates become useless. Not surprisingly, parameter redundancy, or indistinguishability between parameters, drives multicollinearity and should be avoided whenever possible. Here we list a few examples of multicollinearity.