sses represent a small fraction of the total number ever produced. To add to the difficulty of estimating the number of variation units, there is no agreed upon way to delimit their boundaries.
 Despite these problems, it is possible to get a rough idea of how many variation units would occur in Hebrews if a large number of the extant witnesses were compared. Since 1997, the Institute for New Testament Textual Research (INTF) in Münster, Germany, has been publishing installments of its Editio Critica Maior (ECM). The fascicles are so far limited to the Catholic Letters, where a total of 3061 variation units have been identified. Based on respective word counts of 7591 and 4953 for the Catholic Letters and Hebrews, this figure implies that the ECM installment for Hebrews will contain about 2000 variation units. It follows that the 44 variation units given in the UBS4 apparatus of Hebrews represent a small sample of the total number. In view of this, it becomes important to know what sampling error applies for a distance calculated from a selection of variation units.
 If a sample is selected from a population then a sample statistic can be calculated to estimate a population parameter. In the present case, the population is all variation units of Hebrews, the sample is those variation units presented in the UBS4 apparatus, the sample statistic is the simple matching distance (i.e. proportion of disagreements) between two witnesses calculated from the sample of variation units, and the population parameter is the actual distance that would be obtained if all variation units were considered.
 A statistical analysis can be used to obtain an interval (i.e. range) of distances that one is confident contains the actual distance. This “confidence interval” specifies minimum and maximum distances between which the actual value is expected to lie. In order to obtain a confidence interval, a “confidence level” is selected then the boundaries of an interval are computed such that if many samples were randomly chosen, a proportion of confidence intervals equal to the confidence level would contain the actual value. A popular confidence level for the present kind of work is 95%.
 If one had a comprehensive list of variations units for extant witnesses, the actual distance between two witnesses could be calculated by selecting the set of variation units where the readings of both witnesses are defined then calculating the proportion of disagreement for this set. For each variation unit where the readings of both witnesses are defined, it is only possible for the respective readings to agree or disagree. The set of all agreements and disagreements could therefore be represented as a set of observations, each observation signifying agreement or disagreement at the corresponding variation unit. If many random samples of a particular size were taken from this set and the number of disagreements in each sample counted then the result would be a distribution of numbers of disagreement. Provided that certain conditions are met, this “sampling distribution” is approximately the same as the binomial distribution corresponding to the given number of observations and the probability of disagreement in the selected variation units.
 Once the sampling distribution is known, the limits of the confidence interval for the number of disagreements are found as follows:
Select a confidence level (e.g. 0.95).
Select upper and lower cumulative probabilities such that the difference between them is the confidence level (e.g. 0.025 and 0.975).
Choose two witnesses to compare.
Select the set of variation units where the readings of both witnesses are defined.
Count the number of variation units (n) and calculate the probability of disagreement (p) for the set.
Using the binomial distribution for n observations and probability of “success” p, find the corresponding upper and lower critical limits.
 The resulting confidence interval includes the critical limits and all distances between them. To give an example, there are three disagreements among the sample of