Use of harmonic guiding potentials is perhaps the most commonly adopted method for implementing steered molecular dynamics (SMD) simulations, performed to obtain potentials of mean force (PMFs) of molecular systems using Jarzynski's equality and other non-equilibrium work (NEW) theorems. Harmonic guiding potentials are also the natural choice in single molecule force spectroscopy experiments such as optical tweezers and atomic force microscopy, performed to find the potential of mean force using NEW theorems. The stiff spring approximation (SSA) of Schulten and coworkers enables to use the work performed along many SMD trajectories in Jarzynski's equality to obtain the PMF.We discuss and demonstrate how a high spring constant, k, required for the validity of the SSA can violate another requirement of this theory, i.e., the validity of Brownian dynamics of the system under study, if the value of k is too high. Violation of the Brownian condition results in the introduction of kinetic energy contributions to the external work, performed during SMD simulations.These inertial effects result in skewed work distributions, rather than the Gaussian distributions predicted by SSA. The inertial effects also result in broader work distributions, which in turn worsen the effect of the skewness when one tries to calculate reliable work averages. Remarkably, neither the skewness nor the broadening of work distributions can be attributed to factors other than using too-stiff springs. In particular, our results strongly suggest that the skew and width of work distributions are independent of the average drift velocity and the asymmetries of the physical systems studied, at least for the range of systems and velocities we examined.The skew and broadening of work distributions result in biased estimation of the underlying PMF, using Jarzynski's equality or the more efficient forward-reverse (FR) method of Kosztin and coworkers. This pathology is more pronounced in larger biomolecular simulations, where longer samplings are required to achieve convergence. The bias manifests in such simulations in the form of a systematic error that increases with simulation time. We discuss the proper upper limit for k, such that the inertial effects are practically avoided. Used together with the relation for the lower limit of k (which follows naturally from considering thermal fluctuations per degree of freedom of the system), the practitioner of SMD can then conduct accurate steering, while satisfying the Brownian dynamics requirements of SSA. Even in cases where inertial effects (due to high k) result in biased estimations of the PMF, we argue and demonstrate that using the peak-value (rather than the statistical mean) of the work distributions vastly reduces the bias in the calculated PMFs 2 and improves the accuracy.