This paper presents the ways that sample rate, sample time, and number of test replications can affect the random uncertainty in a measurement. Typical steady timewise experiments seek the average values of measured variables. Even in this case, sample rate and sample time can affect the signal standard deviations and yield different random uncertainty estimates. In addition, many random error sources vary slowly relative to the test time and take on a single value. Test replications can convert systematic uncertainties to random uncertainties by allowing their values to change from test to test. The goal is to record individual tests at a sample rate and time that capture the short timescale error sources, and to replicate tests on the scale of long timescale error sources. This paper presents how to leverage these effects to reduce the overall uncertainty of a measured result without increasing the cost of the experiment.