The testing of any scientific hypothesis requires collecting data. What to measure, when, and with what equipment all affect our predictive capabilities. Measurements are fundamentally uncertain, and handling these errors is of paramount importance. As always, the specifics will change with the system being studied and cost considerations of the experimenters, but we can look to mathematics to shed light on these critical questions. Several systems from biology, epidemiology, and physics will be shown as motivating examples.