Many biological systems and chemical reactions are inherently interactive, and the polymerase chain reaction (PCR) is a notable example. The qualities of the components in PCR explain this interactivity. Free nucleotides, template DNA input, and PCR product are charge-attracted to the magnesium ions required for a functional polymerase. Whereas overall salt content in the reaction can serve to neutralize the influence of the nucleic acid charge, too much or too little salt interferes with the processivity of the polymerase. In addition, secondary and tertiary structures, and guanine-cytosine (G-C) content in the template DNA and PCR product can interfere with the ability of primers to bind to the correct target. In such an instance, a commonplace approach is to add disruptive agents such as DMSO to the reaction that also interferes with the activity of the DNA polymerase. Due to these complex interactions, many researchers resort to simplistic optimization schemes such as varying free magnesium concentration or altering the annealing temperature in the cycling protocol. If the simple approach fails, a common “solution” is to search for alternative target sites and start over with a new set of conditions. This approach may not suit all investigators and may not be possible for certain reactions. With difficult PCR reactions, a lack of robustness may result in a failure to subsequently repeat findings if the operator, the equipment, or the laboratory changes. Here we demonstrate the use of a statistical manufacturing method to make PCR optimization more robust. Based on the statistics developed by Taguchi, we show reliable PCR amplification with greater sensitivity than previously published by others using primers and a target gene in the G-C rich herpes simplex virus type 1 (HSV-1) genome. When applying the same statistical method to a non-interactive enzymatic reaction, there was no indication for the need to change the reaction components…