 For example, a significant error occurring every second of a test can be con- sidered a frequently occurring error. But if that was the only error that occurred for the next hour, the error cannot be considered to be a frequently occurring error. Consider a mathematical equation that divides the total phase variations (TE values) by the total test time; you will realize that if there are less phase variations over a long time, this equation produces a low value. However, the result from the equation increases if there are more phase variations dur- ing the same test time. This sort of equation is used for TDEV calculations to determine the quality of the clock. The low frequency phase variations could appear to be occurring randomly. Only if such events are occurring less often over a longer time can these be marked as outliers. And this results in gaining confidence in the long-term quality of the clock. Think of tossing five coins simultaneously, and with the first toss, all five coins come down heads! Without tossing those coins a lot more times, you cannot be sure if that was an outlier (a random event) or if something strange is wrong with the coins. The TDEV of a clock defines the limit of any randomness (or outliers) in the behavior of a clock. The degree of this randomness is a measure of the low frequency phase variation (wander), which can be statistically calculated from TIE measurements and plotted on graphs, using a TDEV calculation. According to ITU-T G.810, TDEV is defined as "a measure of the expected time variation of a sig- nal as a function of integration time." So, TDEV is particularly useful in revealing the presence of several noise processes in clocks over a given time interval; and this calculated measurement is compared to the limit (specified as a mask in the recommendations) for each clock type. Usually, instability of a system is expressed in terms of some statistical representation of its per- formance. Mathematically, variance or standard deviation are usual metrics that would provi