 the scripts employed to derive results for this article are available upon request. As it happens, the same critical limits are obtained using the binomial distribution with a probability of disagreement equal to the mean probability of disagreement taken across the 23 variation units. Chapter three of Timothy J. Finney, Analysis of Textual Variation, http://purl.org/tfinney/ATV/, provides a comparison of results obtained by binomial, exact, and Monte Carlo calculations.
 Various refinements have been made to the definition of a variation unit. See, for example, those by Ernest C. Colwell and Ernest W. Tune, “Method in Classifying and Evaluating Variant Readings” in Studies in Methodology in Textual Criticism of the New Testament (New Testament Tools and Studies 9; Leiden: Brill, 1969) 98-9; Eldon J. Epp, “Toward the Clarification of the Term 'Textual Variant'” in Studies in the Theory and Method of New Testament Textual Criticism (ed. Eldon J. Epp and Gordon D. Fee; Studies and Documents 45; Grand Rapids: Eerdmans) 60-1; and Gordon D. Fee, “On the Types, Classification, and Presentation of Textual Variation” in Studies in the Theory and Method, 62-6.
 Aland, Barbara, Kurt Aland†, Gerd Mink, Holger Strutwolf, and Klaus Wachtel, eds, Novum Testamentum Graecum Editio Critica Maior: IV: Catholic Letters (Stuttgart: Deutsche Bibelgesellschaft, 1997-). Dr Klaus Wachtel of the INTF kindly supplied the number of variation units.
 There is no guarantee that the interval will contain the actual distance for a given sample: the actual value is either inside or outside the calculated confidence interval. One cannot be sure that a confidence interval contains the actual value unless the entire population is examined.
 See David S. Moore and George P. McCabe, Introduction to the Practice of Statistics (2nd ed.; New York: Freeman, 1993) 372-4. There are four conditions: (1) a fixed number of observations; (2) the observations are independent; (3) there are only two possible outcomes for each observation; and (4) the probability of a “success” (i.e. disagreement) is the same for each observation. The last condition is approximately true if the population size is much larger than the sample size.
 These limits can be obtained using the R expression
qbinom(c(0.025, 0.975), 23, 3/23).
 Moore and McCabe, Introduction, 582.
 These were selected from the output of an R script written to estimate relative interval widths for a range of sample sizes. The relative width is obtained by dividing the number of distinct counts in the interval by the total number of possible counts. For example, the limits of the confidence interval for a sample size of fifteen are 4 and 11, representing eight distinct counts (i.e. 4, 5, 6, ..., 9, 10, 11). As there are sixteen possible counts (i.e. 0, 1, 2, ..., 13, 14, 15), the relative width of the interval is 8/16 or 50%.
 The 54 objects are comprised of 53 witnesses and the object representing the UBS text.
The constraint upon the minimum sample size requires that all pairs of witnesses share at
least twelve variation units where the readings of both are defined (i.e. are not marked
as indeterminate using
NA). Witnesses that do not satisfy the constraint are
dropped using the following algorithm:
Eliminate from the data matrix all witnesses defined in less than the required number of variation units.
Construct a table that records the number of shared variation units in which both witnesses are defined for each pair of remaining witnesses.
For each pair with an insufficient number, eliminate from the data matrix that witness with the least number of shared and defined variation units taken across all remaining witnesses.
Repeat the second and third steps until no pairs with an insufficient number remain.
 W. N. Venables and B. D. Ripley, Modern Applied Statistics with S (4th ed.; New York: Springer, 2002) 306.
 Christopher Chatfield and Alexander J. Collins, Introduction to Multivariate Analysis (London: Chapman and Hall, 1980) 212.
 Ibid., 216.
 The “Divisive Coefficient” indicates the degree of clustering in