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The accepted definition of frequency stability in the time domain is the two-sample variance (or Allan variance). It is based on the measurement of average frequencies over adjacent time intervals, with no "dead time" between the intervals. The primary advantages of the Allan variance are that (1) it is convergent for many encountered noise models for which the conventional variance is divergent; (2) it can distinguish between many important and different spectral noise types; (3) the two-sample approach relates to many practical implementations; for example, the rms change of an oscillator's frequency from one period to the next; and (4) Allan variances can be easily estimated at integer multiples of the sample interval. In 1974 a table of bias functions which related variance estimates with various configurations of number of samples and dead time to the Allan variance was published [l]. The tables were based on noises with pure power-law spectral densities. Often situations occur that unavoidably have dead time between measurements, but still the conventional variances are not convergent. Some of these applications are outside of the time-andfrequency field. Also, the dead times are often distributed throughout a given average, and this distributed dead time is not treated in the 1974 tables. This paper reviews the bias functions B,(N,r,p), and BZ(r,p) and introduces a new bias function, B,(2,M,r,p), to handle the commonly occurring cases of the effect of distributed dead time on the computed variances. Some convenient and easy-to-interpret asymptotic limits are reported. A set of tables for the bias functions are included at the end of this paper.

Variances based on data with dead time between the measurements