de an adequate view to express such instability. However, for clocks, it has been shown that the usual variance or standard deviation calculation has a problem-the calculation does not converge. The idea of convergence can be explained with a generic example. A dataset composed of a large collection of values with a Gaussian distribution will have a specific mean and variance. The mean shows the average value, and the variance shows how closely the values cluster around the mean- much like the stability of a clock. Now, if one were to calculate these values using a small subset or sample of these values, then the level of confidence in those metrics would be low-much like the toss of five coins. And as these parameters are calculated using larger and larger samples of this ex- ample dataset, the confidence in these metrics increases. The increase in confidence here means that variance moves toward a true value for the given dataset -this is referred to as convergence of variance. However, for clocks and oscillators, no matter how large the sample of variations is, apparently there is no evidence of this variance value conver- ging. To deal with this deficiency, one of the alternative approaches is to use TDEV. Rather than understanding the exact formula for calculating TDEV, it might be better to see the TDEV measure as a root mean square (RMS) type of metric to capture clock noise. Mathematically, RMS is calculated by first taking a mean (average) of square values of the sample data and then tak- ing a square root of it. The key aspect of this calculation is that it averages out extremes or outliers in the sample (much more than the normal mean). Interestingly, the extremes average out more and more as the sample data increases. Thus, TDEV becomes a highly averaged value. And if the noise in the clock is occurring rarely, the degree (or magnitude) of this rarely occurring error starts diminishing over longer periods of meas- urement. An example might be something occurring rarely or seldom, such as errors introd