It is well known that size-based scheduling policies, which take into account job size (i.e., the time it takes to run them), can perform very desirably in terms of both response time and fairness. Unfortunately, the requirement of knowing a priori the exact job size is a major obstacle which is frequently insurmountable in practice. Often, it is possible to get a coarse estimation of job size, but unfortunately analytical results with inexact job sizes are challenging to obtain, and simulation-based studies show that several size-based algorithm are severely impacted by job estimation errors. For example, Shortest Remaining Processing Time (SRPT), which yields optimal mean sojourn time when job sizes are known exactly, can drastically underperform when it is fed inexact job sizes. Some algorithms have been proposed to better handle size estimation errors, but they are somewhat complex and this makes their analysis challenging. We consider Shortest Processing Time (SPT), a simplification of SRPT that skips the update of "remaining" job size and results in a preemptive algorithm that simply schedules the job with the shortest estimated processing time. When job size is inexact, SPT performs comparably to the best known algorithms in the presence of errors, while being definitely simpler. In this work, SPT is evaluated through simulation, showing near-optimal performance in many cases, with the hope that its simplicity can open the way to analytical evaluation even when inexact inputs are considered.